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1
Choi, Jai Won, and Balgobin Nandram. "Large Sample Problems." International Journal of Statistics and Probability 10, no. 2 (2021): 81. http://dx.doi.org/10.5539/ijsp.v10n2p81.
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Variance is very important in test statistics as it measures the degree of reliability of estimates. It depends not only on the sample size but also on other factors such as population size, type of data and its distribution, and method of sampling or experiments. But here, we assume that these other fasctors are fixed, and that the test statistic depends only on the sample size.
 
 When the sample size is larger, the variance will be smaller. Smaller variance makes test statistics larger or gives more significant results in testing a hypothesis. Whatever the hypothesis is, it does not matter. Thus, the test result is often misleading because much of it reflects the sample size. Therefore, we discuss the large sample problem in performing traditional tests and show how to fix this problem.
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Armstrong, Richard A. "Is there a large sample size problem?" Ophthalmic and Physiological Optics 39, no. 3 (2019): 129–30. http://dx.doi.org/10.1111/opo.12618.
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Kumar, A. "The Sample Size." Journal of Universal College of Medical Sciences 2, no. 1 (2014): 45–47. http://dx.doi.org/10.3126/jucms.v2i1.10493.
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Finding an "appropriate sample size" has been the most basic and foremost problem; a research worker is always faced with, in all sampling based analytical researches. This is so, since a very large sized sample results to unnecessary wastage of resources, while a very small sized sample may affect adversely the accuracy of sample estimates and thus in turn losing the very efficacy of selected sampling plan. The present paper attempts to highlight the main determinant factors and the analytical approach towards estimation ofrequired sample size, along with a few illustrations. DOI: http://dx.doi.org/10.3126/jucms.v2i1.10493 Journal of Universal College of Medical Sciences (2014) Vol.2(1): 45-47
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Barreiro-Ures, Daniel, Ricardo Cao, and Mario Francisco-Fernández. "Bandwidth Selection in Nonparametric Regression with Large Sample Size." Proceedings 2, no. 18 (2018): 1166. http://dx.doi.org/10.3390/proceedings2181166.
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In the context of nonparametric regression estimation, the behaviour of kernel methods such as the Nadaraya-Watson or local linear estimators is heavily influenced by the value of the bandwidth parameter, which determines the trade-off between bias and variance. This clearly implies that the selection of an optimal bandwidth, in the sense of minimizing some risk function (MSE, MISE, etc.), is a crucial issue. However, the task of estimating an optimal bandwidth using the whole sample can be very expensive in terms of computing time in the context of Big Data, due to the computational complexity of some of the most used algorithms for bandwidth selection (leave-one-out cross validation, for example, has O ( n 2 ) complexity). To overcome this problem, we propose two methods that estimate the optimal bandwidth for several subsamples of our large dataset and then extrapolate the result to the original sample size making use of the asymptotic expression of the MISE bandwidth. Preliminary simulation studies show that the proposed methods lead to a drastic reduction in computing time, while the statistical precision is only slightly decreased.
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Feldmann, Rodney M. "Decapod Crustacean Paleobiogeography: Resolving the Problem of Small Sample Size." Short Courses in Paleontology 3 (1990): 303–15. http://dx.doi.org/10.1017/s2475263000001847.
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Studies of paleobiogeography have changed markedly in recent decades transforming a once static subject into one which now has great potential as a useful counterpart to systematic and ecological studies in the interpretation of the geological history of organisms. This has resulted, in large part, from the emergence of plate tectonic models which, in turn, have been used as the bases for extremely sophisticated paleoclimatic modeling. As a result, paleobiogeography has attained a level of precision comparable to that of the studies of paleoecology and systematic paleontology. It is now possible to consider causes for global patterns of origin and dispersal of organisms on a much more realistic level than was previously possible.
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Heckmann, T., K. Gegg, A. Gegg, and M. Becht. "Sample size matters: investigating the effect of sample size on a logistic regression debris flow susceptibility model." Natural Hazards and Earth System Sciences Discussions 1, no. 3 (2013): 2731–79. http://dx.doi.org/10.5194/nhessd-1-2731-2013.
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Abstract. Predictive spatial modelling is an important task in natural hazard assessment and regionalisation of geomorphic processes or landforms. Logistic regression is a multivariate statistical approach frequently used in predictive modelling; it can be conducted stepwise in order to select from a number of candidate independent variables those that lead to the best model. In our case study on a debris flow susceptibility model, we investigate the sensitivity of model selection and quality to different sample sizes in light of the following problem: on the one hand, a sample has to be large enough to cover the variability of geofactors within the study area, and to yield stable results; on the other hand, the sample must not be too large, because a large sample is likely to violate the assumption of independent observations due to spatial autocorrelation. Using stepwise model selection with 1000 random samples for a number of sample sizes between n = 50 and n = 5000, we investigate the inclusion and exclusion of geofactors and the diversity of the resulting models as a function of sample size; the multiplicity of different models is assessed using numerical indices borrowed from information theory and biodiversity research. Model diversity decreases with increasing sample size and reaches either a local minimum or a plateau; even larger sample sizes do not further reduce it, and approach the upper limit of sample size given, in this study, by the autocorrelation range of the spatial datasets. In this way, an optimised sample size can be derived from an exploratory analysis. Model uncertainty due to sampling and model selection, and its predictive ability, are explored statistically and spatially through the example of 100 models estimated in one study area and validated in a neighbouring area: depending on the study area and on sample size, the predicted probabilities for debris flow release differed, on average, by 7 to 23 percentage points. In view of these results, we argue that researchers applying model selection should explore the behaviour of the model selection for different sample sizes, and that consensus models created from a number of random samples should be given preference over models relying on a single sample.
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Qin, S., and G. E. O. Widera. "Determination of Sample Size in Service Inspection." Journal of Pressure Vessel Technology 119, no. 1 (1997): 57–60. http://dx.doi.org/10.1115/1.2842267.
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When performing inservice inspection on a large volume of identical components, it becomes an almost impossible task to inspect all those in which defects may exist, even if their failure probabilities are known. As a result, an appropriate sample size needs to be determined when setting up an inspection program. In this paper, a probabilistic analysis method is employed to solve this problem. It is assumed that the characteristic data of components has a certain distribution which can be taken as known when the mean and standard deviations of serviceable and defective sets of components are estimated. The sample size can then be determined within an acceptable assigned error range. In this way, both false rejection and acceptance can be avoided with a high degree of confidence.
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Heckmann, T., K. Gegg, A. Gegg, and M. Becht. "Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows." Natural Hazards and Earth System Sciences 14, no. 2 (2014): 259–78. http://dx.doi.org/10.5194/nhess-14-259-2014.
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Abstract. Predictive spatial modelling is an important task in natural hazard assessment and regionalisation of geomorphic processes or landforms. Logistic regression is a multivariate statistical approach frequently used in predictive modelling; it can be conducted stepwise in order to select from a number of candidate independent variables those that lead to the best model. In our case study on a debris flow susceptibility model, we investigate the sensitivity of model selection and quality to different sample sizes in light of the following problem: on the one hand, a sample has to be large enough to cover the variability of geofactors within the study area, and to yield stable and reproducible results; on the other hand, the sample must not be too large, because a large sample is likely to violate the assumption of independent observations due to spatial autocorrelation. Using stepwise model selection with 1000 random samples for a number of sample sizes between n = 50 and n = 5000, we investigate the inclusion and exclusion of geofactors and the diversity of the resulting models as a function of sample size; the multiplicity of different models is assessed using numerical indices borrowed from information theory and biodiversity research. Model diversity decreases with increasing sample size and reaches either a local minimum or a plateau; even larger sample sizes do not further reduce it, and they approach the upper limit of sample size given, in this study, by the autocorrelation range of the spatial data sets. In this way, an optimised sample size can be derived from an exploratory analysis. Model uncertainty due to sampling and model selection, and its predictive ability, are explored statistically and spatially through the example of 100 models estimated in one study area and validated in a neighbouring area: depending on the study area and on sample size, the predicted probabilities for debris flow release differed, on average, by 7 to 23 percentage points. In view of these results, we argue that researchers applying model selection should explore the behaviour of the model selection for different sample sizes, and that consensus models created from a number of random samples should be given preference over models relying on a single sample.
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Jaki, Thomas, Minjung Kim, Andrea Lamont, et al. "The Effects of Sample Size on the Estimation of Regression Mixture Models." Educational and Psychological Measurement 79, no. 2 (2018): 358–84. http://dx.doi.org/10.1177/0013164418791673.
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Regression mixture models are a statistical approach used for estimating heterogeneity in effects. This study investigates the impact of sample size on regression mixture’s ability to produce “stable” results. Monte Carlo simulations and analysis of resamples from an application data set were used to illustrate the types of problems that may occur with small samples in real data sets. The results suggest that (a) when class separation is low, very large sample sizes may be needed to obtain stable results; (b) it may often be necessary to consider a preponderance of evidence in latent class enumeration; (c) regression mixtures with ordinal outcomes result in even more instability; and (d) with small samples, it is possible to obtain spurious results without any clear indication of there being a problem.
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Thomas, Hoben. "Effect Size Standard Errors for the Non-Normal Non-Identically Distributed Case." Journal of Educational Statistics 11, no. 4 (1986): 293–303. http://dx.doi.org/10.3102/10769986011004293.
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Suppose there are k independent studies and for each study the experimental and control groups have been sampled from independent but essentially arbitrary populations. The problem is to construct a plausible standard error of the effect size mean (effect sizes are standardized experimental-control group mean differences) when given only minimal sample statistic information. Standard errors based on the sample standard error, or bootstrap, will typically be much too large and have very large variance. A normal theory estimator may prove practically useful in more general settings. Asymptotic distribution-free estimators are provided for two cases.
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Wedyan, Mohammad, Alessandro Crippa, and Adel Al-Jumaily. "A Novel Virtual Sample Generation Method to Overcome the Small Sample Size Problem in Computer Aided Medical Diagnosing." Algorithms 12, no. 8 (2019): 160. http://dx.doi.org/10.3390/a12080160.
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Deep neural networks are successful learning tools for building nonlinear models. However, a robust deep learning-based classification model needs a large dataset. Indeed, these models are often unstable when they use small datasets. To solve this issue, which is particularly critical in light of the possible clinical applications of these predictive models, researchers have developed approaches such as virtual sample generation. Virtual sample generation significantly improves learning and classification performance when working with small samples. The main objective of this study is to evaluate the ability of the proposed virtual sample generation to overcome the small sample size problem, which is a feature of the automated detection of a neurodevelopmental disorder, namely autism spectrum disorder. Results show that our method enhances diagnostic accuracy from 84%–95% using virtual samples generated on the basis of five actual clinical samples. The present findings show the feasibility of using the proposed technique to improve classification performance even in cases of clinical samples of limited size. Accounting for concerns in relation to small sample sizes, our technique represents a meaningful step forward in terms of pattern recognition methodology, particularly when it is applied to diagnostic classifications of neurodevelopmental disorders. Besides, the proposed technique has been tested with other available benchmark datasets. The experimental outcomes showed that the accuracy of the classification that used virtual samples was superior to the one that used original training data without virtual samples.
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Yin, Qingbo, Ehsan Adeli, Liran Shen, and Dinggang Shen. "Population-guided large margin classifier for high-dimension low-sample-size problems." Pattern Recognition 97 (January 2020): 107030. http://dx.doi.org/10.1016/j.patcog.2019.107030.
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Chakraborty, R. "A class of population genetic questions formulated as the generalized occupancy problem." Genetics 134, no. 3 (1993): 953–58. http://dx.doi.org/10.1093/genetics/134.3.953.
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Abstract In categorical genetic data analysis when the sampling units are classified into an arbitrary number of distinct classes, sometimes the sample size may not be large enough to apply large sample approximations for hypothesis testing purposes. Exact sampling distributions of several statistics are derived here, using combinatorial approaches parallel to the classical occupancy problem to help overcome this difficulty. Since the multinomial probabilities can be unequal, this situation is described as a generalized occupancy problem. The sampling properties derived are used to examine nonrandomness of occurrence of mutagen-induced mutations across loci, to devise tests of Hardy-Weinberg proportions of genotype frequencies in the presence of a large number of alleles, and to provide a global test of gametic phase disequilibrium of several restriction site polymorphisms.
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Liu, Xin, and Wei Fan. "Research on the Data Segmentation Technique of Large Rapid Prototyping Sample Based on Features." Applied Mechanics and Materials 248 (December 2012): 551–54. http://dx.doi.org/10.4028/www.scientific.net/amm.248.551.
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Because of the small workbench molding size of rapid prototyping equipment, the processing of large rapid prototyping samples is a problem during the new product development process, for example, motorcycle covering. The relative merits of accuracy engraving technique and rapid prototyping technique during processing are discussed. The method combining accuracy engraving machine and rapid prototyping machine to processing new motorcycle cover samples is proposed. And the surface data segmentation technique based on features is adopt to divide the large rapid prototyping sample reasonably, and then the small parts are collaged after respectively processing, so the problem of large rapid prototyping sample cannot once molding is solved, the speed of new product development is accelerated, the cost of new product development is decreased, the rapid manufacturing is realized. This method has been applied to the processing of new motorcycle cover samples and the application method is expounded.
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HUANG, HONG, JIANWEI LI, and HAILIANG FENG. "SUBSPACES VERSUS SUBMANIFOLDS: A COMPARATIVE STUDY IN SMALL SAMPLE SIZE PROBLEM." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 03 (2009): 463–90. http://dx.doi.org/10.1142/s0218001409007168.
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Automatic face recognition is a challenging problem in the biometrics area, where the dimension of the sample space is typically larger than the number of samples in the training set and consequently the so-called small sample size problem exists. Recently, neuroscientists emphasized the manifold ways of perception, and showed the face images may reside on a nonlinear submanifold hidden in the image space. Many manifold learning methods, such as Isometric feature mapping, Locally Linear Embedding, and Locally Linear Coordination are proposed. These methods achieved the submanifold by collectively analyzing the overlapped local neighborhoods and all claimed to be superior to such subspace methods as Eigenfaces and Fisherfaces in terms of classification accuracy. However, in literature, no systematic comparative study for face recognition is performed among them. In this paper, we carry out a comparative study in face recognition among them, and the study considers theoretical aspects as well as simulations performed using CMU PIE and FERET face databases.
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Zhao, Tan, Jincai Huang, Jianmai Shi, and Chao Chen. "Route Planning for Military Ground Vehicles in Road Networks under Uncertain Battlefield Environment." Journal of Advanced Transportation 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/2865149.
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Route planning for military ground vehicles in the uncertain battlefield is a special kind of route planning problem, as the military vehicles face a great of uncertain and unpredicted attacks. This paper models these uncertainties in the road network by a set of discrete scenarios. A kth shortest-path method is introduced to find intact routes from the origin to the destination for each vehicle. A binary integer programming is presented to formulate the problem. As the combination of the uncertainties results in a huge number of scenarios, we employed the sample average approximation method to obtain a robust solution for the problem. The solution approach is illustrated and tested through three road networks with different scales. The computational results show that, for networks of small scale, our method can provide a good solution with a sample of small size, while, for the large network, with sample of small size, this method usually leads to a suboptimal solution, but a good solution can still be obtained as the sample size grows bigger. In addition, variation trend of the deviation with different sample size indicates that a sample of larger size can bring more stability to the results.
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Gnedin, Alexander V. "Sequential selection of an increasing subsequence from a sample of random size." Journal of Applied Probability 36, no. 04 (1999): 1074–85. http://dx.doi.org/10.1017/s0021900200017873.
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A random number of independent identically distributed random variables is inspected in strict succession. As a variable is inspected, it can either be selected or rejected and this decision becomes final at once. The selected sequence must increase. The problem is to maximize the expected length of the selected sequence. We demonstrate decision policies which approach optimality when the number of observations becomes in a sense large and show that the maximum expected length is close to an easily computable value.
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Gnedin, Alexander V. "Sequential selection of an increasing subsequence from a sample of random size." Journal of Applied Probability 36, no. 4 (1999): 1074–85. http://dx.doi.org/10.1239/jap/1032374756.
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A random number of independent identically distributed random variables is inspected in strict succession. As a variable is inspected, it can either be selected or rejected and this decision becomes final at once. The selected sequence must increase. The problem is to maximize the expected length of the selected sequence.We demonstrate decision policies which approach optimality when the number of observations becomes in a sense large and show that the maximum expected length is close to an easily computable value.
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Raudys, Šarūnas, Aistis Raudys, and Židrina Pabarškaitė. "SUSTAINABLE ECONOMY INSPIRED LARGE-SCALE FEED-FORWARD PORTFOLIO CONSTRUCTION." Technological and Economic Development of Economy 20, no. 1 (2014): 79–96. http://dx.doi.org/10.3846/20294913.2014.889773.
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To understand large-scale portfolio construction tasks we analyse sustainable economy problems by splitting up large tasks into smaller ones and offer an evolutional feed-forward system-based approach. The theoretical justification for our solution is based on multivariate statistical analysis of multidimensional investment tasks, particularly on relations between data size, algorithm complexity and portfolio efficacy. To reduce the dimensionality/sample size problem, a larger task is broken down into smaller parts by means of item similarity – clustering. Similar problems are given to smaller groups to solve. Groups, however, vary in many aspects. Pseudo randomly-formed groups compose a large number of modules of feed-forward decision-making systems. The evolution mechanism forms collections of the best modules for each single short time period. Final solutions are carried forward to the global scale where a collection of the best modules is chosen using a multiclass cost-sensitive perceptron. Collected modules are combined in a final solution in an equally weighted approach (1/N Portfolio). The efficacy of the novel decision-making approach was demonstrated through a financial portfolio optimization problem, which yielded adequate amounts of real world data. For portfolio construction, we used 11,730 simulated trading robot performances. The dataset covered the period from 2003 to 2012 when environmental changes were frequent and largely unpredictable. Walk-forward and out-of-sample experiments show that an approach based on sustainable economy principles outperforms benchmark methods and that shorter agent training history demonstrates better results in periods of a changing environment.
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Sabagh-Sabbagh, Sigem, and David A. Pineda. "Cognitive inhibitory control and arithmetic word problem solving in children with attention deficit/ hyperactivity disorder: a pilot study." Universitas Psychologica 9, no. 3 (2010): 761–72. http://dx.doi.org/10.11144/javeriana.upsy9-3.cica.
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A sample of 30 subjects, 10 with Attention Deficit and Hyperactivity Disorder (ADHD) and 20 non-ADHD children, statistically controlled by age, gender, academic grades and normal full scale intelligence quotient, was selected. To measure cognitive inhibitory control, a math problem solving ability test containing four problems for each level with verbal and numerical irrelevant content was administered. ADHD children exhibited significantly inferior performance in choosing correct answers (p = 0.011) with a large effect size (d = 1.00) and a significantly superior number of irrelevant answers (p = 0.004) with a very large effect size. In conclusion ADHD children showed a cognitive inhibitory control disorder, measured by math problem solving ability.
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Lin, Jin Shyong, Shih Hsun Chen, Ker Jer Huang, Chien Wan Hun, and Chien Chon Chen. "Challenges to Fabricate Large Size-Controllable Submicron-Structured Anodic-Aluminum-Oxide Film." Atlas Journal of Materials Science 2, no. 2 (2017): 65–72. http://dx.doi.org/10.5147/ajms.v2i2.126.
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Anodic aluminum oxide (AAO) is well known for its unique controllable structure and functional contributions in research and developments. However, before AAO can be widely used in the industry, some engineering problems should be overcome. In this study, we designed a novel electrochemical mold, which can resolve the exothermal problem for large-size aluminum sheets during high-voltage anodization process. AAO film with a large sample size of 11 x 11 cm2 in area, 148 μm in thickness and 450 nm in average pore diameter, decorated with ordered-pattern structure, was successfully obtained through a 200 V anodization process. It was noticed that the local heat was generated with increasing the anodizing voltage, resulting in undesired pits and burr defects on the AAO surface. In order to retain AAO’s quality and reduce the producing cost of the anodization process, a mass producing system combining with an overhead conveyor was proposed. The convenient anodization system, novel electrochemical mold and bath may help to fabricate high-quality AAO films efficiently.
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Pang, Yan Yan, Hai Ping Zhu, and Fan Mao Liu. "Fault Diagnosis Method Based on KPCA and Selective Neural Network Ensemble." Advanced Materials Research 915-916 (April 2014): 1272–76. http://dx.doi.org/10.4028/www.scientific.net/amr.915-916.1272.
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Aiming at the problems of less study sample, large network scale and long training time existing in current fault diagnosis field, we develop a new method based on KPCA and selective neural network ensemble. First, reducing the data size by using KPCA to extract the sample features. Then achieving a selective neural network ensemble method based on improved binary particle swarm optimization algorithm (IBPSOSEN), and combining the two methods for fault diagnosis. In selective neural network algorithm, bagging method is used to take a number of different training sets of fault samples to solve the problem of less fault samples. Finally, simulation experiments and comparisons over Tennessee Eastman Process (TE) demonstrate the effectiveness and feasibility of the proposed method.
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Olea, Julio, Juan Ramón Barrada, Francisco J. Abad, Vicente Ponsoda, and Lara Cuevas. "Computerized Adaptive Testing: The Capitalization on Chance Problem." Spanish journal of psychology 15, no. 1 (2012): 424–41. http://dx.doi.org/10.5209/rev_sjop.2012.v15.n1.37348.
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This paper describes several simulation studies that examine the effects of capitalization on chance in the selection of items and the ability estimation in CAT, employing the 3-parameter logistic model. In order to generate different estimation errors for the item parameters, the calibration sample size was manipulated (N = 500, 1000 and 2000 subjects) as was the ratio of item bank size to test length (banks of 197 and 788 items, test lengths of 20 and 40 items), both in a CAT and in a random test. Results show that capitalization on chance is particularly serious in CAT, as revealed by the large positive bias found in the small sample calibration conditions. For broad ranges of θ, the overestimation of the precision (asymptotic Se) reaches levels of 40%, something that does not occur with the RMSE (θ). The problem is greater as the item bank size to test length ratio increases. Potential solutions were tested in a second study, where two exposure control methods were incorporated into the item selection algorithm. Some alternative solutions are discussed.
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Akter, Noor Jahan, and Md Hasinur Rahaman Khan. "Effect of Sample Size on the Profile Likelihood Estimates for Two-stage Hierarchical Linear Models." Journal of Biomedical Analytics 1, no. 2 (2018): 81–89. http://dx.doi.org/10.30577/jba.2018.v1n2.26.
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Determining sample size to produce accurate parameter estimates and make valid inferences about population parameter is a pivotal problem in any well planned research. The issue of sample size becomes more complex in hierarchical linear models because the units at different levels are hierarchically nested. Many studies have been carried out to determine sample sizes at different levels of a nested data model. However, most of the studies assume that the sample size is large enough to conduct the significance test. Some alternative methods can be used to relax the assumption of large sample. Profile likelihood method is more robust in case of small sample and when the variance components need to be estimated. In this study, we investigate the effect of sample sizes on the performance of parameter estimates at the group-level and individual-level of a two-level regression model. We consider a more appropriate statistical approach, profile likelihood method, to check the reliability of estimates of fixed coefficients and variance components.
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Núñez-Antón, Vicente, Juan Manuel Pérez-Salamero González, Marta Regúlez-Castillo, and Carlos Vidal-Meliá. "Improving the Representativeness of a Simple Random Sample: An Optimization Model and Its Application to the Continuous Sample of Working Lives." Mathematics 8, no. 8 (2020): 1225. http://dx.doi.org/10.3390/math8081225.
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This paper proposes an optimization model for selecting a larger subsample that improves the representativeness of a simple random sample previously obtained from a population larger than the population of interest. The problem formulation involves convex mixed-integer nonlinear programming (convex MINLP) and is, therefore, NP-hard. However, the solution is found by maximizing the size of the subsample taken from a stratified random sample with proportional allocation and restricting it to a p-value large enough to achieve a good fit to the population of interest using Pearson’s chi-square goodness-of-fit test. The paper also applies the model to the Continuous Sample of Working Lives (CSWL), which is a set of anonymized microdata containing information on individuals from Spanish Social Security records and the results prove that it is possible to obtain a larger subsample from the CSWL that (far) better represents the pensioner population for each of the waves analyzed.
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Brown, Bryan W., and Roberto S. Mariano. "Predictors in Dynamic Nonlinear Models: Large-Sample Behavior." Econometric Theory 5, no. 3 (1989): 430–52. http://dx.doi.org/10.1017/s0266466600012603.
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The large-sample behavior of one-period-ahead and multiperiod-ahead predictors for a dynamic nonlinear simultaneous system is examined in this paper. Conditional on final values of the endogenous variables, the asymptotic moments of the deterministic, closed-form, Monte Carlo stochastic, and several variations of the residual-based stochastic predictor are analyzed. For one-period-ahead prediction, the results closely parallel our previous findings for static nonlinear systems. For multiperiod-ahead prediction similar results hold, except that the effective number of sample-period residuals available for use with the residual-based predictor is T/m, where T denotes sample size. In an attempt to avoid the problems associated with sample splitting, the complete enumeration predictor is proposed which is a multiperiod-ahead generalization of the one-period-ahead residual-based predictor. A bootstrap predictor is also introduced which is similar to the multiperiod-ahead Monte Carlo except disturbance proxies are drawn from the empirical distribution of the residuals. The bootstrap predictor is found to be asymptotically inefficient relative to both the complete enumeration and Monte Carlo predictors.
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Campelo, Felipe, and Elizabeth F. Wanner. "Sample size calculations for the experimental comparison of multiple algorithms on multiple problem instances." Journal of Heuristics 26, no. 6 (2020): 851–83. http://dx.doi.org/10.1007/s10732-020-09454-w.
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Abstract This work presents a statistically principled method for estimating the required number of instances in the experimental comparison of multiple algorithms on a given problem class of interest. This approach generalises earlier results by allowing researchers to design experiments based on the desired best, worst, mean or median-case statistical power to detect differences between algorithms larger than a certain threshold. Holm’s step-down procedure is used to maintain the overall significance level controlled at desired levels, without resulting in overly conservative experiments. This paper also presents an approach for sampling each algorithm on each instance, based on optimal sample size ratios that minimise the total required number of runs subject to a desired accuracy in the estimation of paired differences. A case study investigating the effect of 21 variants of a custom-tailored Simulated Annealing for a class of scheduling problems is used to illustrate the application of the proposed methods for sample size calculations in the experimental comparison of algorithms.
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Hall, Thomas W., Terri L. Herron, Bethane Jo Pierce, and Terry J. Witt. "The Effectiveness of Increasing Sample Size to Mitigate the Influence of Population Characteristics in Haphazard Sampling." AUDITING: A Journal of Practice & Theory 20, no. 1 (2001): 169–85. http://dx.doi.org/10.2308/aud.2001.20.1.169.
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Over 40 years ago both Deming (1954) and Arkin (1957) expressed concerns that the composition of samples chosen through haphazard selection may be unrepresentative due to the presence of unintended selection biases. To mitigate this problem some experts in the field of audit sampling recommend increasing sample sizes by up to 100 percent when utilizing haphazard selection. To examine the effectiveness of this recommendation 142 participants selected haphazard samples from two populations. The compositions of these samples were then analyzed to determine if certain population elements were overrepresented, and if the extent of overrepresentation declined as sample size increased. Analyses disclosed that certain population elements were overrepresented in the samples. Also, increasing sample size produced no statistically significant change in the composition of samples from one population, while in the second population increasing the sample size produced a statistically significant but minor reduction in overrepresentation. These results suggest that individuals may be incapable of complying with audit guidelines that haphazard sample selections be made without regard to the observable physical features of population elements and cast doubt on the effectiveness of using larger sample sizes to mitigate the problem. Given these findings, standard-setting bodies should reconsider the conditions under which haphazard sampling is sanctioned as a reliable audit tool.
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Bergamaschi, Luca, Giuseppe Gambolati, and Giorgio Pini. "Spectral Analysis of Large Finite Element Problems by Optimization Methods." Shock and Vibration 1, no. 6 (1994): 529–40. http://dx.doi.org/10.1155/1994/427192.
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Recently an efficient method for the solution of the partial symmetric eigenproblem (DACG, deflated-accelerated conjugate gradient) was developed, based on the conjugate gradient (CG) minimization of successive Rayleigh quotients over deflated subspaces of decreasing size. In this article four different choices of the coefficientβkrequired at each DACG iteration for the computation of the new search directionPkare discussed. The “optimal” choice is the one that yields the same asymptotic convergence rate as the CG scheme applied to the solution of linear systems. Numerical results point out that the optimalβkleads to a very cost effective algorithm in terms of CPU time in all the sample problems presented. Various preconditioners are also analyzed. It is found that DACG using the optimalβkand (LLT)−1as a preconditioner, L being the incomplete Cholesky factor of A, proves a very promising method for the partial eigensolution. It appears to be superior to the Lanczos method in the evaluation of the 40 leftmost eigenpairs of five finite element problems, and particularly for the largest problem, with size equal to 4560, for which the speed gain turns out to fall between 2.5 and 6.0, depending on the eigenpair level.
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McMorris, B. J., L. A. Raynor, K. A. Monsen, and K. E. Johnson. "What big size you have! Using effect sizes to determine the impact of public health nursing interventions." Applied Clinical Informatics 04, no. 03 (2013): 434–44. http://dx.doi.org/10.4338/aci-2013-07-ra-0044.
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Summary Background: The Omaha System is a standardized interface terminology that is used extensively by public health nurses in community settings to document interventions and client outcomes. Researchers using Omaha System data to analyze the effectiveness of interventions have typically calculated p-values to determine whether significant client changes occurred between admission and discharge. However, p-values are highly dependent on sample size, making it difficult to distinguish statistically significant changes from clinically meaningful changes. Effect sizes can help identify practical differences but have not yet been applied to Omaha System data. Methods: We compared p-values and effect sizes (Cohen’s d) for mean differences between admission and discharge for 13 client problems documented in the electronic health records of 1,016 young low-income parents. Client problems were documented anywhere from 6 (Health Care Supervision) to 906 (Caretaking/parenting) times. Results: On a scale from 1 to 5, the mean change needed to yield a large effect size (Cohen’s d 0.80) was approximately 0.60 (range = 0.50 – 1.03) regardless of p-value or sample size (i.e., the number of times a client problem was documented in the electronic health record). Conclusions: Researchers using the Omaha System should report effect sizes to help readers determine which differences are practical and meaningful. Such disclosures will allow for increased recognition of effective interventions.
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Kim, Hajin, Myeong-Seon Gil, Yang-Sae Moon, and Mi-Jung Choi. "Variable size sampling to support high uniformity confidence in sensor data streams." International Journal of Distributed Sensor Networks 14, no. 4 (2018): 155014771877399. http://dx.doi.org/10.1177/1550147718773999.
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In order to rapidly process large amounts of sensor stream data, it is effective to extract and use samples that reflect the characteristics and patterns of the data stream well. In this article, we focus on improving the uniformity confidence of KSample, which has the characteristics of random sampling in the stream environment. For this, we first analyze the uniformity confidence of KSample and then derive two uniformity confidence degradation problems: (1) initial degradation, which rapidly decreases the uniformity confidence in the initial stage, and (2) continuous degradation, which gradually decreases the uniformity confidence in the later stages. We note that the initial degradation is caused by the sample range limitation and the past sample invariance, and the continuous degradation by the sampling range increase. For each problem, we present a corresponding solution, that is, we provide the sample range extension for sample range limitation, the past sample change for past sample invariance, and the use of UC-window for sampling range increase. By reflecting these solutions, we then propose a novel sampling method, named UC-KSample, which largely improves the uniformity confidence. Experimental results show that UC-KSample improves the uniformity confidence over KSample by 2.2 times on average, and it always keeps the uniformity confidence higher than the user-specified threshold. We also note that the sampling accuracy of UC-KSample is higher than that of KSample in both numeric sensor data and text data. The uniformity confidence is an important sampling metric in sensor data streams, and this is the first attempt to apply uniformity confidence to KSample. We believe that the proposed UC-KSample is an excellent approach that adopts an advantage of KSample, dynamic sampling over a fixed sampling ratio, while improving the uniformity confidence.
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Kobayashi, Ken, Naoki Hamada, Akiyoshi Sannai, Akinori Tanaka, Kenichi Bannai, and Masashi Sugiyama. "Bézier Simplex Fitting: Describing Pareto Fronts of´ Simplicial Problems with Small Samples in Multi-Objective Optimization." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2304–13. http://dx.doi.org/10.1609/aaai.v33i01.33012304.
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Multi-objective optimization problems require simultaneously optimizing two or more objective functions. Many studies have reported that the solution set of an M-objective optimization problem often forms an (M − 1)-dimensional topological simplex (a curved line for M = 2, a curved triangle for M = 3, a curved tetrahedron for M = 4, etc.). Since the dimensionality of the solution set increases as the number of objectives grows, an exponentially large sample size is needed to cover the solution set. To reduce the required sample size, this paper proposes a Bézier simplex model and its fitting algorithm. These techniques can exploit the simplex structure of the solution set and decompose a high-dimensional surface fitting task into a sequence of low-dimensional ones. An approximation theorem of Bézier simplices is proven. Numerical experiments with synthetic and real-world optimization problems demonstrate that the proposed method achieves an accurate approximation of high-dimensional solution sets with small samples. In practice, such an approximation will be conducted in the postoptimization process and enable a better trade-off analysis.
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Scharf, Andreas, Wolfgang Kretschmer, Gerhard Morgenroth, et al. "Radiocarbon Dating of Iron Artifacts at the Erlangen AMS Facility." Radiocarbon 46, no. 1 (2004): 175–80. http://dx.doi.org/10.1017/s0033822200039497.
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One problem in preparing iron for radiocarbon dating is the low carbon content which makes the sample size needed too large for some sample combustion systems. Also, the metallic character of the samples complicates sample combustion or oxidation. The Erlangen accelerator mass spectrometry group uses an elemental analyzer for the sample combustion, directly followed by a reduction facility. As the carbon content and sample size for iron samples are unsuitable for combustion in an elemental analyzer, 2 alternative approaches are to (a) avoid oxidation and reduction, or (b) extract the carbon from the iron, prior to combustion. Therefore, 2 different pathways were explored. One is direct sputtering of the unprocessed iron sample in the ion source. The other is the complete chemical extraction of carbon from the iron sample and dating of the carbonaceous residue. Also, different methods for cleaning samples and removing contamination were tested. In Erlangen, a Soxhlet extraction is employed for this purpose. Also, the sampling of the iron sample by drilling or cutting can be a source of contamination. Thus, the measurement of iron drill shavings yielded ages that were far too high. The first results for iron samples of known age from 2 archaeological sites in Germany are presented and discussed.
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Chen, Danmin, Shuai Yang, and Funa Zhou. "Transfer Learning Based Fault Diagnosis with Missing Data Due to Multi-Rate Sampling." Sensors 19, no. 8 (2019): 1826. http://dx.doi.org/10.3390/s19081826.
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Deep learning is an effective feature extraction method widely applied in fault diagnosis fields since it can extract fault features potentially involved in multi-sensor data. But different sensors equipped in the system may sample data at different sampling rates, which will inevitably result in a problem that a very small number of samples with a complete structure can be used for deep learning since the input of a deep neural network (DNN) is required to be a structurally complete sample. On the other hand, a large number of samples are required to ensure the efficiency of deep learning based fault diagnosis methods. To solve the problem that a structurally complete sample size is too small, this paper proposes a fault diagnosis framework of missing data based on transfer learning which makes full use of a large number of structurally incomplete samples. By designing suitable transfer learning mechanisms, extra useful fault features can be extracted to improve the accuracy of fault diagnosis based simply on structural complete samples. Thus, online fault diagnosis, as well as an offline learning scheme based on deep learning of multi-rate sampling data, can be developed. The efficiency of the proposed method is demonstrated by utilizing data collected from the QPZZ- II rotating machinery vibration experimental platform system.
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Volz, Erik M., and Simon D. W. Frost. "Sampling through time and phylodynamic inference with coalescent and birth–death models." Journal of The Royal Society Interface 11, no. 101 (2014): 20140945. http://dx.doi.org/10.1098/rsif.2014.0945.
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Many population genetic models have been developed for the purpose of inferring population size and growth rates from random samples of genetic data. We examine two popular approaches to this problem, the coalescent and the birth–death-sampling model (BDM), in the context of estimating population size and birth rates in a population growing exponentially according to the birth–death branching process. For sequences sampled at a single time, we found the coalescent and the BDM gave virtually indistinguishable results in terms of the growth rates and fraction of the population sampled, even when sampling from a small population. For sequences sampled at multiple time points, we find that the birth–death model estimators are subject to large bias if the sampling process is misspecified. Since BDMs incorporate a model of the sampling process, we show how much of the statistical power of BDMs arises from the sequence of sample times and not from the genealogical tree. This motivates the development of a new coalescent estimator, which is augmented with a model of the known sampling process and is potentially more precise than the coalescent that does not use sample time information.
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Khan, Sajid Ali, Sayyad Khurshid, Shabnam Arshad, and Owais Mushtaq. "Bias Estimation of Linear Regression Model with Autoregressive Scheme using Simulation Study." Journal of Mathematical Analysis and Modeling 2, no. 1 (2021): 26–39. http://dx.doi.org/10.48185/jmam.v2i1.131.
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In regression modeling, first-order auto correlated errors are often a problem, when the data also suffers from independent variables. Generalized Least Squares (GLS) estimation is no longer the best alternative to Ordinary Least Squares (OLS). The Monte Carlo simulation illustrates that regression estimation using data transformed according to the GLS method provides estimates of the regression coefficients which are superior to OLS estimates. In GLS, we observe that in sample size $200$ and $\sigma$=3 with correlation level $0.90$ the bias of GLS $\beta_0$ is $-0.1737$, which is less than all bias estimates, and in sample size $200$ and $\sigma=1$ with correlation level $0.90$ the bias of GLS $\beta_0$ is $8.6802$, which is maximum in all levels. Similarly minimum and maximum bias values of OLS and GLS of $\beta_1$ are $-0.0816$, $-7.6101$ and $0.1371$, $0.1383$ respectively. The average values of parameters of the OLS and GLS estimation with different size of sample and correlation levels are estimated. It is found that for large samples both methods give similar results but for small sample size GLS is best fitted as compared to OLS.
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Maier, Daniel, Andreas Niekler, Gregor Wiedemann, and Daniela Stoltenberg. "How Document Sampling and Vocabulary Pruning Affect the Results of Topic Models." Computational Communication Research 2, no. 2 (2020): 139–52. http://dx.doi.org/10.5117/ccr2020.2.001.maie.
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Abstract Topic modeling enables researchers to explore large document corpora. Large corpora, however, can be extremely costly to model in terms of time and computing resources. In order to circumvent this problem, two techniques have been suggested: (1) to model random document samples, and (2) to prune the vocabulary of the corpus. Although frequently applied, there has been no systematic inquiry into how the application of these techniques affects the respective models. Using three empirical corpora with different characteristics (news articles, websites, and Tweets), we systematically investigated how different sample sizes and pruning affect the resulting topic models in comparison to models of the full corpora. Our inquiry provides evidence that both techniques are viable tools that will likely not impair the resulting model. Sample-based topic models closely resemble corpus-based models if the sample size is large enough (> 10,000 documents). Moreover, extensive pruning does not compromise the quality of the resultant topics.
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Lazic, Stanley E. "Four simple ways to increase power without increasing the sample size." Laboratory Animals 52, no. 6 (2018): 621–29. http://dx.doi.org/10.1177/0023677218767478.
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Underpowered experiments have three problems: true effects are harder to detect, the true effects that are detected tend to have inflated effect sizes and as power decreases so does the probability that a statistically significant result represents a true effect. Many biology experiments are underpowered and recent calls to change the traditional 0.05 significance threshold to a more stringent value of 0.005 will further reduce the power of the average experiment. Increasing power by increasing the sample size is often the only option considered, but more samples increases costs, makes the experiment harder to conduct and is contrary to the 3Rs principles for animal research. We show how the design of an experiment and some analytical decisions can have a surprisingly large effect on power.
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Yang, Yangyang, Zhi Ying Ren, Hongbai Bai, Ding Shen, and Bin Zhang. "Study on the Mechanical Properties of Metal Rubber Inner Core of O-Type Seal with Large Ring-to-Diameter Ratio." Advances in Materials Science and Engineering 2020 (February 29, 2020): 1–12. http://dx.doi.org/10.1155/2020/2875947.
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In view of the problems of ordinary rubber seals, such as high- and low-temperature resistance, easy aging, and insufficient load-bearing performance, O-type metal rubber seals with large ring-to-diameter ratio were prepared by the cold stamping method using stainless steel wire as raw material. The effects of heat treatment and porosity on the compression and tensile properties of test samples were investigated. Under uniaxial compression testing, it was found that the test sample had typical hysteresis characteristics, and the loss factor and energy dissipation of the sample with the same size and different porosity increased with the decrease of porosity. The loss factor and energy dissipation of the heat-treated sample were lower than those of the untreated sample. Thus, the smaller the porosity, the greater the change of loss factor and energy dissipation. Under uniaxial tensile testing, obvious stage changes were found during the tensile process, which included a linear elasticity stage, the formation and development stage of wire breakage, the one-by-one fracture stage of wires, and the complete failure stage of the sample. The yield strength, ultimate tensile strength, and modulus of elasticity of four samples with different porosity were measured, and it was found that the three parameters increased with the decrease of porosity. Moreover, the thermal treatment conductivity increased with the decrease of porosity. The aforementioned three parameters were generally increased. This indicated that metal rubber materials have good mechanical properties under high-temperature environments, which effectively solves the problem of vulnerability to aging and failure of ordinary rubbers under normal working conditions and has strong practical engineering significance.
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Gao, Jing. "Decision Tree Generation Algorithm without Pruning." Applied Mechanics and Materials 441 (December 2013): 731–37. http://dx.doi.org/10.4028/www.scientific.net/amm.441.731.
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On the generation of decision tree based on rough set model, for the sake of classification accuracy, existing algorithms usually partition examples too specific. And it is hard to avoid the negative impact caused by few special examples on decision tree. In order to obtain this priority in traditional decision tree algorithm based on rough set, the sample is partitioned much more meticulously. Inevitably, a few exceptional samples have negative effect on decision tree. And this leads that the generated decision tree seems too large to be understood. It also reduces the ability in classifying and predicting the coming data. To settle these problems, the restrained factor is introduced in this paper. For expanding nodes in generating decision tree algorithm, besides traditional terminating condition, an additional terminating condition is involved when the restrained factor of sample is higher than a given threshold, then the node will not be expanded any more. Thus, the problem of much more meticulous partition is avoided. Furthermore, the size of decision tree generated with restrained factor involved will not seem too large to be understood.
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Schmeling, Martina, Donald S. Burnett, Amy J. G. Jurewicz, and Igor V. Veryovkin. "Steps toward accurate large-area analyses of Genesis solar wind samples: evaluation of surface cleaning methods using total reflection X-ray fluorescence spectrometry." Powder Diffraction 27, no. 2 (2012): 75–78. http://dx.doi.org/10.1017/s0885715612000346.
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Total reflection X-ray fluorescence spectrometry (TXRF) was used to analyze residual surface contamination on Genesis solar wind samples and to evaluate different cleaning methods. To gauge the suitability of a cleaning method, two samples were analyzed following cleaning by lab-based TXRF. The analysis comprised an overview and a crude manual mapping of the samples by orienting them with respect to the incident X-ray beam in such a way that different regions were covered. The results show that cleaning with concentrated hydrochloric acid and a combination of hydrochloric acid and hydrofluoric acid decreased persistent inorganic contaminants substantially on one sample. The application of CO2 snow for surface cleaning tested on the other sample appears to be effective in removing one persistent Genesis contaminant, namely germanium. Unfortunately, the TXRF analysis results of the second sample were impacted by relatively high background contamination. This was mostly due to the relatively small sample size and that the solar wind collector was already mounted with silver glue for resonance ion mass spectrometry (RIMS) on an aluminium stub. Further studies are planned to eliminate this problem. In an effort to identify the location of very persistent contaminants, selected samples were also subjected to environmental scanning electron microscopy. The results showed excellent agreement with TXRF analysis.
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Zhang, Shuai, Jian Zhang, Shui Guang Tong, and Chao Wei Wu. "The Study of Large Equipments Condition Maintenance Policy Based on Kalman Filtering." Advanced Materials Research 706-708 (June 2013): 2128–32. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.2128.
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Aimed at the problem that because common proportional hazards model (PHM) cannot fuse new failure data of long-life complex equipment, which features a small-sample, the reliability estimation accuracy will decline, a new condition-based maintenance strategy based on dynamic PHM was proposed. Kalman filtering theory was adopted to fuse in-time new failure data and expand sample size. Extended Kalman filtering method was used to solve the nonlinearity of the observation equation of PHM and then its regression coefficient was online updated, according to which the residual life was estimated and the optimal maintenance decision was made. Finally, the condition monitoring data and historic operation data of a certain kind of wind power gearbox were used to validate this method. The result indicates that this method has good dynamic estimation ability under the condition of small sample with a 20.6% increase in the accuracy of regression coefficient estimation and a 8.7% decrease in optimal preventive maintenance interval estimation error.
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Bode, Peter. "Kilogram Sample Analysis by Nuclear Analytical Techniques: Complementary Opportunities for the Mineral and Geosciences." Minerals 11, no. 5 (2021): 443. http://dx.doi.org/10.3390/min11050443.
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Sample-size reduction including homogenization is often required to obtain a test portion for element compositional analysis. Analyses of replicate test portions may provide insight into the sampling constant, and often much larger quantities are needed to limit the contribution of sampling error. In addition, it cannot be demonstrated that the finally obtained test portion is truly representative of the originally collected material. Nuclear analytical techniques such as neutron and photon activation analysis and (neutron-induced) prompt gamma activation analyses can now be used to study and overcome these analytical problems. These techniques are capable of obtaining multi-element measurements from irregularly shaped objects with masses ranging from multiple grams to multiple kilograms. Prompt gamma analysis can be combined with neutron tomography, resulting in position-sensitive information. The analysis of large samples provides unprecedented complementary opportunities for the mineral and geosciences. It enables the experimental assessment of the representativeness of test portions of the originally collected material, as well as the analysis of samples that are not allowed to be sub-sampled or dissolved, the analysis of materials that are difficult to be homogenized at large, and studies on the location of inhomogeneities. Examples of such applications of large-sample analyses are described herein.
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Dramais, Guillaume, Benoît Camenen, Jérôme Le Coz, et al. "Comparison of standardized methods for suspended solid concentration measurements in river samples." E3S Web of Conferences 40 (2018): 04018. http://dx.doi.org/10.1051/e3sconf/20184004018.
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SSC (Suspended Solid Concentration) measurements in rivers are a complex scientific issue. Many questions arise on the spatial and temporal distribution of particles throughout a cross-section, on the properties of particles and grain-size, and also on the sediment transport capacity of streams and rivers. In this study, we focused on the SSC and grain size distribution measured from river samples, automatically or manually acquired. Many agencies suggested slightly different methods for measuring SSC: The European standard NF EN 872, which related to the US EPA 160.2 requires sub-sampling using shake-and-pour aliquot selection. The APHA 2540D requires sub-sampling by pipetting at middepth in the original sample shaken with a magnetic stirrer. These methods lead to significant uncertainty when particles larger than 63 μm are present in the samples. The ASTM D3977 analysis method, endorsed by the USGS is more accurate to capture and quantify particles larger than 63 μm. In this study we confirm the sub-sampling problem in a large concentration range using a set of samples from an alpine river.
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Bandyopadhyay, Uttam, and Gopaldeb Chattopadhyay. "Inverse Sampling for Bivariate Nonparametric Two-Sample Problems Against Restricted Alternatives." Calcutta Statistical Association Bulletin 42, no. 3-4 (1992): 221–36. http://dx.doi.org/10.1177/0008068319920306.
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The object of the present investigation is to provide suitable nonparametric tests for testing the identity of two bivariate distribution functions F1 and F2 against a class of restricted alternatives when there is sample of fixed size m from F1 and the observations from F2 are drawn sequentially. Various large sample results of the proposed tests are formulated and examined. AMS Subject Classification: Primary 62010, Secondary 62020.
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Gnecco, Giorgio, Federico Nutarelli, and Daniela Selvi. "Optimal trade-off between sample size, precision of supervision, and selection probabilities for the unbalanced fixed effects panel data model." Soft Computing 24, no. 21 (2020): 15937–49. http://dx.doi.org/10.1007/s00500-020-05317-5.
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Abstract This paper is focused on the unbalanced fixed effects panel data model. This is a linear regression model able to represent unobserved heterogeneity in the data, by allowing each two distinct observational units to have possibly different numbers of associated observations. We specifically address the case in which the model includes the additional possibility of controlling the conditional variance of the output given the input and the selection probabilities of the different units per unit time. This is achieved by varying the cost associated with the supervision of each training example. Assuming an upper bound on the expected total supervision cost and fixing the expected number of observed units for each instant, we analyze and optimize the trade-off between sample size, precision of supervision (the reciprocal of the conditional variance of the output) and selection probabilities. This is obtained by formulating and solving a suitable optimization problem. The formulation of such a problem is based on a large-sample upper bound on the generalization error associated with the estimates of the parameters of the unbalanced fixed effects panel data model, conditioned on the training input dataset. We prove that, under appropriate assumptions, in some cases “many but bad” examples provide a smaller large-sample upper bound on the conditional generalization error than “few but good” ones, whereas in other cases the opposite occurs. We conclude discussing possible applications of the presented results, and extensions of the proposed optimization framework to other panel data models.
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Tan, Monique, Feng J. He, and Graham A. MacGregor. "Salt and cardiovascular disease in PURE: A large sample size cannot make up for erroneous estimations." Journal of the Renin-Angiotensin-Aldosterone System 19, no. 4 (2018): 147032031881001. http://dx.doi.org/10.1177/1470320318810015.
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The latest Prospective Urban Rural Epidemiology (PURE) study claims that salt reduction should be confined to settings where its intake exceeds 12.7 g/day and that eating less than 11.1 g/day of salt could increase cardiovascular risk. More specifically, Mente et al. suggested that (a) salt intake was positively associated with stroke only when it exceeded 12.7 g/day, (b) salt intake was inversely associated with myocardial infarction and total mortality, and (c) these associations were largely independent of blood pressure. These provocative findings challenge the robust evidence on the role of salt reduction in the prevention of cardiovascular disease and call into question the World Health Organization’s global recommendation to reduce salt intake to less than 5 g/day. However, Mente et al.’s re-analysis of the PURE data has several severe methodological problems, including erroneous estimations of salt intake from a single spot urine using the problematic Kawasaki formula. As such, these implausible results cannot be used to refute the strong evidence supporting the benefits of salt reduction for the general population worldwide.
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Smith, Paul L., Donna V. Kliche, and Roger W. Johnson. "The Bias and Error in Moment Estimators for Parameters of Drop Size Distribution Functions: Sampling from Gamma Distributions." Journal of Applied Meteorology and Climatology 48, no. 10 (2009): 2118–26. http://dx.doi.org/10.1175/2009jamc2114.1.
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Abstract This paper complements an earlier one that demonstrated the bias in the method-of-moments (MM) estimators frequently used to estimate parameters for drop size distribution (DSD) functions being “fitted” to observed raindrop size distributions. Here the authors consider both the bias and the errors in MM estimators applied to samples from known gamma DSDs (of which the exponential DSD treated in the earlier paper is a special case). The samples were generated using a similar Monte Carlo simulation procedure. The skewness in the sampling distributions of the DSD moments that causes this bias is less pronounced for narrower population DSDs, and therefore the bias problems (and also the errors) diminish as the gamma shape parameter increases. However, the bias still increases with the order of the moments used in the MM procedures; thus it is stronger when higher-order moments (such as the radar reflectivity) are used. The simulation results also show that the errors of the estimates of the DSD parameters are usually larger when higher-order moments are employed. As a consequence, only MM estimators using the lowest-order sample moments that are thought to be well determined should be used. The biases and the errors of most of the MM parameter estimates diminish as the sample size increases; with large samples the moment estimators may become sufficiently accurate for some purposes. Nevertheless, even with some fairly large samples, MM estimators involving high-order moments can yield parameter values that are physically implausible or are incompatible with the input observations. Correlations of the sample moments with the size of the largest drop in a sample (Dmax) are weaker than for the case of sampling from an exponential DSD, as are the correlations of the MM-estimated parameters with Dmax first noted in that case. However, correlations between the estimated parameters remain because functions of the same observations are correlated. These correlations generally strengthen as the sample size increases.
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Suvittawat, Adisak. "Effective Inventory Management of Entrepreneurs in Eastern Part Thailand: 10 Big Questions." Information Management and Business Review 7, no. 1 (2015): 67–71. http://dx.doi.org/10.22610/imbr.v7i1.1140.
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The objective of this research is finding the variables which effect on entrepreneurs’ inventory management in eastern part of Thailand. The entrepreneurs have been identified in 2 difference sizes, large and small as the inventory management variables must be different. The results show that inventory management of large size were accepted by the entrepreneurs are space utilization, product sample, product feature differentiation, effective inventory management system, inventory management objective, effective inventory picking system, inventory cost reduction, regular problem happen, effective IT system and specific soft ware for inventory management. The results show that inventory management of small size were accepted by the entrepreneurs are specific soft ware for inventory management, effective IT system, inventory management objective, effective inventory picking system, regular problem happen, product feature differentiation, inventory cost reduction, product sample, space utilization and effective inventory management. Inventory management in small firm and big firm has significantly priorities as the big firms have advantage in space utilization but need specific soft ware for inventory management due to big firms have more products or inventories items than small firms.
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LU, WEI, ZHENZHOU CHEN, ZHENGAN YAO, and LEI LI. "FAST CLUSTERING-BASED KERNEL FOLEY–SAMMON TRANSFORM." International Journal of Wavelets, Multiresolution and Information Processing 07, no. 01 (2009): 75–87. http://dx.doi.org/10.1142/s0219691309002805.
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The Kernel Foley–Sammon Transform (KFST) performs well in solving nonlinear discriminant analysis problems. However, as a kernel method, KFST also faces with large kernel matrix calculation problems O(n3) with the sample size n. KFST will be very costly and even intractable to compute when n is large. In this paper, we propose a Fast Clustering-based Kernel Foley–Sammon Transform (FCKFST) approach to tackle this problem. FCKFST solves KFST over a reductive l × l matrix representing clustering data instead of an n × n matrix of the original data, where l is exceedingly smaller than n. This paper also proves that FCKFST improving the calculating efficiency does not decrease the classification precision with comparison to KFST. We apply our method to digit and image recognition problems, and we obtain good experimental results.