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Relevant bibliographies by topics / Logistic regression; Nonparametric; Sample size

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Academic literature on the topic 'Logistic regression; Nonparametric; Sample size'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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Journal articles on the topic "Logistic regression; Nonparametric; Sample size"

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Farrell, Max H., Tengyuan Liang, and Sanjog Misra. "Deep Neural Networks for Estimation and Inference." Econometrica 89, no. 1 (2021): 181–213. http://dx.doi.org/10.3982/ecta16901.

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We study deep neural networks and their use in semiparametric inference. We establish novel nonasymptotic high probability bounds for deep feedforward neural nets. These deliver rates of convergence that are sufficiently fast (in some cases minimax optimal) to allow us to establish valid second‐step inference after first‐step estimation with deep learning, a result also new to the literature. Our nonasymptotic high probability bounds, and the subsequent semiparametric inference, treat the current standard architecture: fully connected feedforward neural networks (multilayer perceptrons), with the now‐common rectified linear unit activation function, unbounded weights, and a depth explicitly diverging with the sample size. We discuss other architectures as well, including fixed‐width, very deep networks. We establish the nonasymptotic bounds for these deep nets for a general class of nonparametric regression‐type loss functions, which includes as special cases least squares, logistic regression, and other generalized linear models. We then apply our theory to develop semiparametric inference, focusing on causal parameters for concreteness, and demonstrate the effectiveness of deep learning with an empirical application to direct mail marketing.

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Chiorean, Angelica Rita, Mădălina Brîndușa Szep, Diana Sorina Feier, Magdalena Duma, Marco Andrei Chiorean, and Ștefan Strilciuc. "Impact of Strain Elastography on BI-RADS classification in small invasive lobular carcinoma." Medical Ultrasonography 20, no. 2 (2018): 148. http://dx.doi.org/10.11152/mu-1272.

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Aims: The purpose of this study was to determine the impact of strain elastography (SE) on the Breast Imaging Reporting Data System (BI-RADS) classification depending on invasive lobular carcinoma (ILC) lesion size.Materials and methods: We performed a retrospective analysis on a sample of 152 female subjects examined between January 2010 – January 2017. SE was performed on all patients and ILC was subsequently diagnosed by surgical or ultrasound-guided biopsy. BI-RADS 1, 2, 6 and Tsukuba BGR cases were omitted. BI-RADS scores were recorded before and after the use of SE. The differences between scores were compared to the ILC tumor size using nonparametric tests and logistic binary regression. We controlled for age, focality, clinical assessment, heredo-collateral antecedents, B-mode and Doppler ultrasound examination. An ROC curve was used to identify the optimal cut-off point for size in relationship to BI-RADS classificationdifference using Youden’s index.Results: The histological subtypes of ILC lesions (n=180) included in the sample were luminal A (70%, n=126), luminal B (27.78%, n=50), triple negative (1.67%, n=3) and HER2+ (0.56%, n=1). The BI-RADS classification was higher when SE was performed (Z=- 6.629, p<0.000). The ROC curve identified a cut-off point of 13 mm for size in relationship to BI-RADS classification difference (J=0.670, p<0.000). Small ILC tumors were 17.92% more likely to influence BI-RADS classification (p<0.000).Conclusions: SE offers enhanced BI-RADS classification in small ILC tumors (<13 mm). Sonoelastography brings added value to B-mode breast ultrasound as an adjacent to mammography in breast cancer screening.

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Hsieh, F. Y. "Sample size tables for logistic regression." Statistics in Medicine 8, no. 7 (1989): 795–802. http://dx.doi.org/10.1002/sim.4780080704.

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Khorshed Alam, M., M. Bhaskara Rao, and Fu-Chih Cheng. "Sample size determination in logistic regression." Sankhya B 72, no. 1 (2010): 58–75. http://dx.doi.org/10.1007/s13571-010-0004-6.

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Motrenko, Anastasiya, Vadim Strijov, and Gerhard-Wilhelm Weber. "Sample size determination for logistic regression." Journal of Computational and Applied Mathematics 255 (January 2014): 743–52. http://dx.doi.org/10.1016/j.cam.2013.06.031.

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Broll, Susanne, Sabine Glaser, and Lothar Kreienbrock. "Calculating sample size bounds for logistic regression." Preventive Veterinary Medicine 54, no. 2 (2002): 105–11. http://dx.doi.org/10.1016/s0167-5877(02)00012-0.

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Demidenko, Eugene. "Sample size determination for logistic regression revisited." Statistics in Medicine 26, no. 18 (2006): 3385–97. http://dx.doi.org/10.1002/sim.2771.

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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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Ali, Amjad, Sabz Ali, Sajjad Ahmad Khan, et al. "Sample size issues in multilevel logistic regression models." PLOS ONE 14, no. 11 (2019): e0225427. http://dx.doi.org/10.1371/journal.pone.0225427.

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Shieh, G. "Sample size calculations for logistic and Poisson regression models." Biometrika 88, no. 4 (2001): 1193–99. http://dx.doi.org/10.1093/biomet/88.4.1193.

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Dissertations / Theses on the topic "Logistic regression; Nonparametric; Sample size"

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Signorini, David F. "Practical aspects of kernel smoothing for binary regression and density estimation." Thesis, n.p, 1998. http://oro.open.ac.uk/19923/.

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Meganathan, Karthikeyan. "Sample Size Determination in Simple Logistic Regression: Formula versus Simulation." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627663458916666.

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Lee, Michelle Oi San. "Sample size calculation for testing an interaction effect in a logistic regression under measurement error model /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?MATH%202003%20LEE.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 66-67). Also available in electronic version. Access restricted to campus users.

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Hadley, Patrick. "The performance of the Mantel-Haenszel and logistic regression dif detection procedures across sample size and effect size: A Monte Carlo study." Thesis, University of Ottawa (Canada), 1995. http://hdl.handle.net/10393/10019.

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In recent years, public attention has become focused on the issue of test and item bias in standardized tests. Since the 1980's, the Mantel-Haenszel (Holland & Thayer, 1986) and Logistic Regression procedures (Swaminathan & Rogers, 1990) have been developed to detect item bias, or differential item functioning (dif). In this study the effectiveness of the MH and LR procedures was compared under a variety of conditions, using simulated data. The ability of the MH and LR to detect dif was tested at sample sizes of 100/100, 200/200, 400/400, 600/600, and 800/800. The simulated test had 66 items, the first 33 items with item discrimination ("a") set at 0.80, the second 33 items with "a" set at 1.20. The pseudo-guessing parameter ("c") was 0.15 for all items. The item difficulty ("b") parameter ranged from $-$2.00 to 2.00 in increments of 0.125 for the first 33 items, and again for the second 33 items. Both the MH and LRU detected dif with a high degree of success whenever sample size was large (600 or more), especially when effect size, no matter how measured, was also large. The LRU outperformed the MH marginally under almost every condition of the study. However, the LRU also had a higher false-positive rate than the MH, a finding consistent with previous studies (Pang et al., 1994, Tian et al., 1994a, 1994b). Since the "a" and "b" parameters which underly the computation of the three measures of effect size used in the study are not always determinable in data derived from real world test administrations, it may be that the $\Delta\sb{\rm MH}$ is the best available measure of effect size in real world test items. (Abstract shortened by UMI.)

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Chen, Mei-Kuang. "Who Are the Cigarette Smokers in Arizona." Thesis, The University of Arizona, 2007. http://hdl.handle.net/10150/193268.

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The purpose of this study was to investigate the relationship between cigarette smoking and socio-demographic variables based on the empirical literature and the primitive theories in the field. Two regression approaches, logistic regression and linear multiple regression, were conducted on the two most recent Arizona Adult Tobacco Surveys to test the hypothesized models. The results showed that cigarette smokers in Arizona are mainly residents who have not completed a four-year college degree, who are unemployed, White, non-Hispanic, or young to middle-aged adults. Among the socio-demographic predictors of interest, education is the most important variable in identifying cigarette smokers, even though the predictive power of these socio-demographic variables is small. Practical and methodological implications of these findings are discussed.

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Kennedy, Michael. "The influence of sample size, effect size, and percentage of DIF items on the performance of the Mantel-Haenszel and logistic regression DIF identification procedures." Thesis, University of Ottawa (Canada), 1994. http://hdl.handle.net/10393/6884.

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The frequent use of standardized tests for admission, advancement, and accreditation has increased public awareness of measurement issues, in particular, test and item bias. The logistic regression (LR) and Mantel-Haenszel (MH) procedures are relatively new methods of detecting item bias or differential item functioning (DIF) in tests. In only a few studies has the performance of these two procedures been compared. In the present study, sample size, effect size, and percentage of DIF items in the test were manipulated in order to compare detection rates of uniform DIF by the LR and MH procedures. Simulated data, with known amounts of DIF, were used to evaluate the effects of these variables on DIF detection rates. In detecting uniform DIF, the LR procedure had a slight advantage over the MH procedure at the cost of increased false positive rates. P-value difference was definitely a more accurate measure of the amount of DIF than b value difference. (Abstract shortened by UMI.)

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Janse, Sarah A. "INFERENCE USING BHATTACHARYYA DISTANCE TO MODEL INTERACTION EFFECTS WHEN THE NUMBER OF PREDICTORS FAR EXCEEDS THE SAMPLE SIZE." UKnowledge, 2017. https://uknowledge.uky.edu/statistics_etds/30.

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In recent years, statistical analyses, algorithms, and modeling of big data have been constrained due to computational complexity. Further, the added complexity of relationships among response and explanatory variables, such as higher-order interaction effects, make identifying predictors using standard statistical techniques difficult. These difficulties are only exacerbated in the case of small sample sizes in some studies. Recent analyses have targeted the identification of interaction effects in big data, but the development of methods to identify higher-order interaction effects has been limited by computational concerns. One recently studied method is the Feasible Solutions Algorithm (FSA), a fast, flexible method that aims to find a set of statistically optimal models via a stochastic search algorithm. Although FSA has shown promise, its current limits include that the user must choose the number of times to run the algorithm. Here, statistical guidance is provided for this number iterations by deriving a lower bound on the probability of obtaining the statistically optimal model in a number of iterations of FSA. Moreover, logistic regression is severely limited when two predictors can perfectly separate the two outcomes. In the case of small sample sizes, this occurs quite often by chance, especially in the case of a large number of predictors. Bhattacharyya distance is proposed as an alternative method to address this limitation. However, little is known about the theoretical properties or distribution of B-distance. Thus, properties and the distribution of this distance measure are derived here. A hypothesis test and confidence interval are developed and tested on both simulated and real data.

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高孝先. "On Sample Size for Nonparametric Regression and Partial Linear Models." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/58839271932925701088.

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Wang, Lifei. "Species Distribution Modeling: Implications of Modeling Approaches, Biotic Effects, Sample Size, and Detection Limit." Thesis, 2013. http://hdl.handle.net/1807/43754.

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When we develop and use species distribution models to predict species' current or potential distributions, we are faced with the trade-offs between model generality, precision, and realism. It is important to know how to improve and validate model generality while maintaining good model precision and realism. However, it is difficult for ecologists to evaluate species distribution models using field-sampled data alone because the true species response function to environmental or ecological factors is unknown. Species distribution models should be able to approximate the true characteristics and distributions of species if ecologists want to use them as reliable tools. Simulated data provide the advantage of being able to know the true species-environment relationships and control the causal factors of interest to obtain insights into the effects of these factors on model performance. I used a case study on Bythotrephes longimanus distributions from several hundred Ontario lakes and a simulation study to explore the effects on model performance caused by several factors: the choice of predictor variables, the model evaluation methods, the quantity and quality of the data used for developing models, and the strengths and weaknesses of different species distribution models. Linear discriminant analysis, multiple logistic regression, random forests, and artificial neural networks were compared in both studies. Results based on field data sampled from lakes indicated that the predictive performance of the four models was more variable when developed on abiotic (physical and chemical) conditions alone, whereas the generality of these models improved when including biotic (relevant species) information. When using simulated data, although the overall performance of random forests and artificial neural networks was better than linear discriminant analysis and multiple logistic regression, linear discriminant analysis and multiple logistic regression had relatively good and stable model sensitivity at different sample size and detection limit levels, which may be useful for predicting species presences when data are limited. Random forests performed consistently well at different sample size levels, but was more sensitive to high detection limit. The performance of artificial neural networks was affected by both sample size and detection limit, and it was more sensitive to small sample size.

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Books on the topic "Logistic regression; Nonparametric; Sample size"

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Michael, Kennedy. The influence of sample size, effect size, and percentage of DIF items on the performance of the Mantel-Haenszel and logistic regression DIF identification procedures. 1994.

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Book chapters on the topic "Logistic regression; Nonparametric; Sample size"

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Vach, Werner. "Quantitative Comparisons: Results of Finite Sample Size Simulation Studies." In Logistic Regression with Missing Values in the Covariates. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-2650-5_6.

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Makalic, Enes, and Daniel Francis Schmidt. "Review of Modern Logistic Regression Methods with Application to Small and Medium Sample Size Problems." In AI 2010: Advances in Artificial Intelligence. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17432-2_22.

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Jennions, Michael D., Christopher J. Lortie, Michael S. Rosenberg, and Hannah R. Rothstein. "Publication and Related Biases." In Handbook of Meta-analysis in Ecology and Evolution. Princeton University Press, 2013. http://dx.doi.org/10.23943/princeton/9780691137285.003.0014.

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This chapter discusses the increased occurrence of publication bias in the scientific literature. Publication bias is associated with the inaccurate representation of the merit of a hypothesis or idea. A strict definition is that it occurs when the published literature reports results that systematically differ from those of all studies and statistical tests conducted; the result is that false conclusions are drawn. The chapter presents five main approaches used to either detect potential narrow sense publication bias or assess how sensitive the results of a meta-analysis are to the possible exclusion. These include funnel plots, tests for relationships between effect size and sample size using nonparametric correlation or regression, trim and fill method, fail-safe numbers, and model selection.

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Conference papers on the topic "Logistic regression; Nonparametric; Sample size"

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Memmedli, Memmedaga, and Munevvere Yildiz. "Comparison study on smoothing parameter and sample size in nonparametric fuzzy local polynomial regression models." In 2012 IV International Conference "Problems of Cybernetics and Informatics" (PCI). IEEE, 2012. http://dx.doi.org/10.1109/icpci.2012.6486400.

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Fitri, Fadhilah, and Bertho Tantular. "Determination of Sample Size on Logistic Regression for Sakernas Data in Jayapura Regency in 2015." In Proceedings of the 2nd International Conference on Mathematics and Mathematics Education 2018 (ICM2E 2018). Atlantis Press, 2018. http://dx.doi.org/10.2991/icm2e-18.2018.5.

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Yan, He, and Susan Fiorito. "Diffusion and Performance of CAD/CAM in the U.S. Textile and Apparel Industry." In ASME 2002 International Mechanical Engineering Congress and Exposition. ASMEDC, 2002. http://dx.doi.org/10.1115/imece2002-39475.

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This study examines the determinants of CAD/CAM adoption and diffusion in American textile and apparel industries. Innovation diffusion theory provided a conceptual framework and empirical base applicable to the study of technology adoption and implementation. A variety of sources were used to develop the survey which was mailed to a national random sample of 500 textile and apparel manufacturers. The responses of 103 manufacturers from 30 different states were analyzed. Factor analysis was used to identify the dimensions of reasons for CAD/CAM adoption. Hypotheses were tested with logistic regression analysis procedures. The diffusion of CAD/CAM practices was found to be driven primarily by the market and affected by the business-unit size. In addition, labor considerations affected recent CAD/CAM adoption.

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Tan, Sibel, Uğur Şimdi, and Bengü Everest. "Analysis of Factors Affecting the Available Agricultural Policy Utilization Levels of Organic Farming Producers: The Case of Izmir Seferhisar Town." In International Conference on Eurasian Economies. Eurasian Economists Association, 2017. http://dx.doi.org/10.36880/c08.01846.

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Within the agricultural policies of the country, supports are provided to producers for the implementation of certain activities. Sufficiency of such supports feedbacks received from the target groups using these supports.  There are 141 agricultural facilities in Seferihisar dealing with organic farming and these facilities constituted the research universe. Full-count method was used to determine the research sample. A face-to-face questionnaire was performed with 100 farmers dealing with organic farming. Basic descriptive statistics were used to put forth the socio-economic status of the farmers, facility characteristics and their current status with regard to use of available agricultural supports. The factors influencing the use of available agricultural supports were analyzed by “Logistic Regression” method.  Logistic regression analysis was performed to find out the utilization levels of available policies by the farmers dealing with organic farming. Farmer age was identified as the most significant factor influencing the utilization level of consultancy services provided by the state. On the other hand, credit utilization was identified as the most significant factor for the deficiency payments and fuel-fertilizer supports. Education levels was the most significant factor in using supports provided for organic farming and age was the most significant factor in using soil analysis supports.  Results revealed age, educational level, credit use capability and land size as the most significant factors in utilization of agricultural policies and state supports. Development of such characteristics of the producers will increase the chance of success of available policies and proper allocation of agricultural supports. 

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Alobahi, Asma, Sumaya Yusuf, Zainab Dookhy, and Vijay Ganji. "Vitamin D is associated with Improved Lung Function But Not with Asthma, Emphysema and Bronchitis." In Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2020. http://dx.doi.org/10.29117/quarfe.2020.0225.

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Background: Hypovitaminosis D has been linked to several non-bone related diseases. Relation between serum 25-hydroxyvitamin D [25(OH)] and lung function and lung diseases has received less attention. Methods: Data from 3 National Health and Nutrition Examination Survey (NHANES) cycles, 2007-2012 were used. The sample size was 11983. Lung function markers such as forced vital capacity (FVC)and forced expiratory volume in 1 second (FEV1) were collected with Spirometry. Relation between serum 25(OH)D and lung function makers was assessed by the multivariate regression. Relation between serum 25(OH)D and prevalence of asthma, emphysema, and chronic bronchitis were assessed with multivariate-adjusted logistic regression. Results: Serum 25(OH)D was significantly associated with FVC and FEV1 (P <0.001). When data were stratified based on sex and smoking status, we found similar associations between serum 25(OH)D and lung function markers. No relation was found between serum 25(OH)D and prevalence of asthma, chronic bronchitis, and emphysema. Conclusions: Serum 25(OH)D is significantly associated with improved lung function markers. Controlled studies are needed to determine if improved serum 25(OH)D will improve the lung function in adults.

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Mikova, Irena, Lenka Komarkova, and Pavel Pudil. "Support of development of non-profit organisations through special training programs for their managers." In Contemporary Issues in Business, Management and Economics Engineering. Vilnius Gediminas Technical University, 2019. http://dx.doi.org/10.3846/cibmee.2019.048.

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Purpose – the paper is concerned with the issue of further education in non-profit organisations (NPOs) using training programs for their managers (TPM) and their impact on organisation development. The study aim was to find out whether and how the existence of TPM in the organisation depends on the founder, sector and duration of its economic activity. It also investigates which competencies the NPO managers perceive as the key ones. Research methodology – the study, based on the research questionnaire, included 69 NPOs. The dependence of TPM on defined characteristics has been assessed using Pearson’s chi-squared test or logistic regression. Conclusions (findings) – the results indicate that the usage of TPM is related to the size and founder of NPO. The other examined characteristics do not have a statistically significant effect. Furthermore, competencies considered by NPO managers as the most important were specified. Research limitations – follow from the size of the research sample. Practical implications – also, NPOs should focus on developing competencies mentioned in the study and evaluate them using impacts on the results of work activities. Originality/Value – the influence of training of managers on the development of NPOs is not sufficiently addressed in the literature

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