Relevant bibliographies by topics / Sample size estimation

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Sample size estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 18 June 2025

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Journal articles on the topic "Sample size estimation"

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Chander, NGopi. "Sample size estimation." Journal of Indian Prosthodontic Society 17, no. 3 (2017): 217. http://dx.doi.org/10.4103/jips.jips_169_17.

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Likhvantsev, V. V., M. Ya Yadgarov, L. B. Berikashvili, K. K. Kadantseva, and A. N. Kuzovlev. "Sample size estimation." Anesteziologiya i reanimatologiya, no. 6 (2020): 77. http://dx.doi.org/10.17116/anaesthesiology202006177.

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Weller, Susan C. "Sample Size Estimation." Field Methods 27, no. 4 (2014): 333–47. http://dx.doi.org/10.1177/1525822x14530086.

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Sivasamy, Shyam. "Sample size considerations in research." Endodontology 35, no. 4 (2023): 304–8. http://dx.doi.org/10.4103/endo.endo_235_23.

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ABSTRACT “What should be the sample size for my study?” is a common question in the minds of every research at some point of the research cycle. Answering this question with confident is tough even for a seasoned researcher. Sample size determination, an important aspect of sampling design consideration of a study, is a factor which directly influences the internal and external validity of the study. Unless the sample size is of adequate size, the results of the study cannot be justified. Conducting a study in too small sample size or too large sample size have ethical, scientific, practical, and economic strings attached to it and have detrimental effects in the research outcomes. A myriad of factors including the study design, type of power analysis, sampling technique employed, and acceptable limits of error fixed play a decisive role in estimating the sample size. However, the advent of free to use software and websites for sample size estimation has actually diluted or sometimes complicated the whole process of sample size estimation as important factors or assumptions related to sample size are overlooked. Engaging a professional biostatistician from the very beginning of the research process would be a wise decision while conducting research. This article highlights the important concepts related to sample size estimation with emphasis on factors which influences it.
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Sharma, Suresh K., Shiv Kumar Mudgal, Rakhi Gaur, Jitender Chaturvedi, Satyaveer Rulaniya, and Priya Sharma. "Navigating Sample Size Estimation for Qualitative Research." Journal of Medical Evidence 5, no. 2 (2024): 133–39. http://dx.doi.org/10.4103/jme.jme_59_24.

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Abstract There are well-established rules and methods about sample size estimation in quantitative research approaches. However, qualitative research approaches justify very little about sample size estimation principles and largely depend on subjective judgements and arbitrariness. Contrarily, an adequate sample size is essential for a study to address the core elements of validity and credibility in qualitative research too such as rigor, trustworthiness, conformability and acceptance. Therefore, this review was carried out to explain the available methods to estimate sample size for qualitative studies. After conducting a thorough literature review, we discovered related articles that explore the estimation of sample size for qualitative studies. By examining these findings and integrating the information with our personal experience for estimation of sample size in the field of qualitative studies, we have produced an all-encompassing narrative review. After an in-depth literature search, four different approaches were described in this paper to answer the question of how to estimate sample size in qualitative studies. The four approaches described in this paper are (a) rules of thumb, (b) conceptual models, (c) concept of saturation and (d) statistics-based methods for sample size estimation in qualitative research. The paper presents four methods for estimating sample size in qualitative studies and simplifies the statistical approach for saturation calculation in qualitative studies. Yet, it is vital to responsibly integrate these methods, acknowledging their limitations and maintaining the importance of sample size estimation in qualitative studies.
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McCormick, Joshua L., and Kevin A. Meyer. "Sample Size Estimation for On-Site Creel Surveys." North American Journal of Fisheries Management 37, no. 5 (2017): 970–80. http://dx.doi.org/10.1080/02755947.2017.1342723.

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Kitikidou, K., and G. Chatzilazarou. "Estimating the sample size for fitting taper equations." Journal of Forest Science 54, No. 4 (2008): 176–82. http://dx.doi.org/10.17221/789-jfs.

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Much work has been done fitting taper equations to describe tree bole shapes, but few researchers have investigated how large the sample size should be. In this paper, a method that requires two variables that are linearly correlated was applied to determine the sample size for fitting taper equations. Two cases of sample size estimation were tested, based on the method mentioned above. In the first case, the sample size required is referred to the total number of diameters estimated in the sampled trees. In the second case, the sample size required is referred to the number of sampled trees. The analysis showed that both methods are efficient from a validity standpoint but the first method has the advantage of decreased cost, since it costs much more to incrementally sample another tree than it does to make another diameter measurement on an already sampled tree.
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Streiner, David L. "Sample-Size Formulae for Parameter Estimation." Perceptual and Motor Skills 78, no. 1 (1994): 275–84. http://dx.doi.org/10.2466/pms.1994.78.1.275.

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Formulae are presented for calculating sample-size requirements when the purpose of the study is to estimate the magnitude of a parameter rather than to test an hypothesis. Formulae are given for the mean, a proportion, and correlation, for the slope, intercept, value of Ȳ, and Y for a given value of X in multiple regression, and for the odds and risk ratios.
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Posch, Martin, Florian Klinglmueller, Franz König, and Frank Miller. "Estimation after blinded sample size reassessment." Statistical Methods in Medical Research 27, no. 6 (2016): 1830–46. http://dx.doi.org/10.1177/0962280216670424.

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Blinded sample size reassessment is a popular means to control the power in clinical trials if no reliable information on nuisance parameters is available in the planning phase. We investigate how sample size reassessment based on blinded interim data affects the properties of point estimates and confidence intervals for parallel group superiority trials comparing the means of a normal endpoint. We evaluate the properties of two standard reassessment rules that are based on the sample size formula of the z-test, derive the worst case reassessment rule that maximizes the absolute mean bias and obtain an upper bound for the mean bias of the treatment effect estimate.
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De Martini, Daniele. "Conservative Sample Size Estimation in Nonparametrics." Journal of Biopharmaceutical Statistics 21, no. 1 (2010): 24–41. http://dx.doi.org/10.1080/10543400903453343.

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Dissertations / Theses on the topic "Sample size estimation"

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Denne, Jonathan S. "Sequential procedures for sample size estimation." Thesis, University of Bath, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.320460.

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Che, Huiwen. "Cutoff sample size estimation for survival data: a simulation study." Thesis, Uppsala universitet, Statistiska institutionen, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-234982.

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This thesis demonstrates the possible cutoff sample size point that balances goodness of es-timation and study expenditure by a practical cancer case. As it is crucial to determine the sample size in designing an experiment, researchers attempt to find the suitable sample size that achieves desired power and budget efficiency at the same time. The thesis shows how simulation can be used for sample size and precision calculations with survival data. The pre-sentation concentrates on the simulation involved in carrying out the estimates and precision calculations. The Kaplan-Meier estimator and the Cox regression coefficient are chosen as point estimators, and the precision measurements focus on the mean square error and the stan-dard error.
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Banton, Dwaine Stephen. "A BAYESIAN DECISION THEORETIC APPROACH TO FIXED SAMPLE SIZE DETERMINATION AND BLINDED SAMPLE SIZE RE-ESTIMATION FOR HYPOTHESIS TESTING." Diss., Temple University Libraries, 2016. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/369007.

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Statistics<br>Ph.D.<br>This thesis considers two related problems that has application in the field of experimental design for clinical trials: • fixed sample size determination for parallel arm, double-blind survival data analysis to test the hypothesis of no difference in survival functions, and • blinded sample size re-estimation for the same. For the first problem of fixed sample size determination, a method is developed generally for testing of hypothesis, then applied particularly to survival analysis; for the second problem of blinded sample size re-estimation, a method is developed specifically for survival analysis. In both problems, the exponential survival model is assumed. The approach we propose for sample size determination is Bayesian decision theoretical, using explicitly a loss function and a prior distribution. The loss function used is the intrinsic discrepancy loss function introduced by Bernardo and Rueda (2002), and further expounded upon in Bernardo (2011). We use a conjugate prior, and investigate the sensitivity of the calculated sample sizes to specification of the hyper-parameters. For the second problem of blinded sample size re-estimation, we use prior predictive distributions to facilitate calculation of the interim test statistic in a blinded manner while controlling the Type I error. The determination of the test statistic in a blinded manner continues to be nettling problem for researchers. The first problem is typical of traditional experimental designs, while the second problem extends into the realm of adaptive designs. To the best of our knowledge, the approaches we suggest for both problems have never been done hitherto, and extend the current research on both topics. The advantages of our approach, as far as we see it, are unity and coherence of statistical procedures, systematic and methodical incorporation of prior knowledge, and ease of calculation and interpretation.<br>Temple University--Theses
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Serrano, Daniel Curran Patrick J. "Error of estimation and sample size in the linear mixed model." Chapel Hill, N.C. : University of North Carolina at Chapel Hill, 2008. http://dc.lib.unc.edu/u?/etd,1653.

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Thesis (M.A.)--University of North Carolina at Chapel Hill, 2008.<br>Title from electronic title page (viewed Sep. 16, 2008). "... in partial fulfillment of the requirements for the degree of Master of Arts in the Department of Psychology." Discipline: Psychology; Department/School: Psychology.
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Song, Juhee. "Bootstrapping in a high dimensional but very low sample size problem." Texas A&M University, 2003. http://hdl.handle.net/1969.1/3853.

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High Dimension, Low Sample Size (HDLSS) problems have received much attention recently in many areas of science. Analysis of microarray experiments is one such area. Numerous studies are on-going to investigate the behavior of genes by measuring the abundance of mRNA (messenger RiboNucleic Acid), gene expression. HDLSS data investigated in this dissertation consist of a large number of data sets each of which has only a few observations. We assume a statistical model in which measurements from the same subject have the same expected value and variance. All subjects have the same distribution up to location and scale. Information from all subjects is shared in estimating this common distribution. Our interest is in testing the hypothesis that the mean of measurements from a given subject is 0. Commonly used tests of this hypothesis, the t-test, sign test and traditional bootstrapping, do not necessarily provide reliable results since there are only a few observations for each data set. We motivate a mixture model having C clusters and 3C parameters to overcome the small sample size problem. Standardized data are pooled after assigning each data set to one of the mixture components. To get reasonable initial parameter estimates when density estimation methods are applied, we apply clustering methods including agglomerative and K-means. Bayes Information Criterion (BIC) and a new criterion, WMCV (Weighted Mean of within Cluster Variance estimates), are used to choose an optimal number of clusters. Density estimation methods including a maximum likelihood unimodal density estimator and kernel density estimation are used to estimate the unknown density. Once the density is estimated, a bootstrapping algorithm that selects samples from the estimated density is used to approximate the distribution of test statistics. The t-statistic and an empirical likelihood ratio statistic are used, since their distributions are completely determined by the distribution common to all subject. A method to control the false discovery rate is used to perform simultaneous tests on all small data sets. Simulated data sets and a set of cDNA (complimentary DeoxyriboNucleic Acid) microarray experiment data are analyzed by the proposed methods.
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Knowlton, Nicholas Scott. "Robust estimation of inter-chip variability to improve microarray sample size calculations." Oklahoma City : [s.n.], 2005.

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Ntambwe, Lupetu Ives. "Sequential sample size re-estimation in clinical trials with multiple co-primary endpoints." Thesis, University of Warwick, 2014. http://wrap.warwick.ac.uk/66339/.

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In this thesis, we consider interim sample size adjustment in clinical trials with multiple co-primary continuous endpoints. We aim to answer two questions: First, how to adjust a sample size in clinical trial with multiple continuous co-primary endpoints using adaptive and group sequential design. Second, how to construct a test in order to control the family-wise type I error rate and maintain the power, even if the correlation ρ between endpoints is not known. To answer the first question, we conduct K different interim tests, each for one endpoint and each at level α/K (i.e. Bonferroni adjustment). To answer the second question, either we perform a sample size re-estimation in which the results of the interim analysis are used to estimate one or more nuisance parameters, and this information is used to determine the sample size for the rest of the trial or the inverse normal combination test type approach; or we conduct a group sequential test where we monitor the information, and the information is adjusted to allow the correlation ρ to be estimated at each stage or the inverse normal combination test type approach. We show that both methods control the family-wise type I error α and maintain the power and that the group sequential methodology seems to be more powerful, as this depends on the spending function.
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Oymak, Okan. "Sample size determination for estimation of sensor detection probabilities based on a test variable." Thesis, Monterey, Calif. : Naval Postgraduate School, 2007. http://bosun.nps.edu/uhtbin/hyperion-image.exe/07Jun%5FOymak.pdf.

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Thesis (M.S. in Operations Research)--Naval Postgraduate School, June 2007.<br>Thesis Advisor(s): Lyn R. Whitaker. "June 2007." Includes bibliographical references (p. 95-96). Also available in print.
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Cong, Danni. "The effect of sample size re-estimation on type I error rates when comparing two binomial proportions." Kansas State University, 2016. http://hdl.handle.net/2097/34504.

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Master of Science<br>Department of Statistics<br>Christopher I. Vahl<br>Estimation of sample size is an important and critical procedure in the design of clinical trials. A trial with inadequate sample size may not produce a statistically significant result. On the other hand, having an unnecessarily large sample size will definitely increase the expenditure of resources and may cause a potential ethical problem due to the exposure of unnecessary number of human subjects to an inferior treatment. A poor estimate of the necessary sample size is often due to the limited information at the planning stage. Hence, the adjustment of the sample size mid-trial has become a popular strategy recently. In this work, we introduce two methods for sample size re-estimation for trials with a binary endpoint utilizing the interim information collected from the trial: a blinded method and a partially unblinded method. The blinded method recalculates the sample size based on the first stage’s overall event proportion, while the partially unblinded method performs the calculation based only on the control event proportion from the first stage. We performed simulation studies with different combinations of expected proportions based on fixed ratios of response rates. In this study, equal sample size per group was considered. The study shows that for both methods, the type I error rates were preserved satisfactorily.
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Zhao, Songnian. "The impact of sample size re-estimation on the type I error rate in the analysis of a continuous end-point." Kansas State University, 2017. http://hdl.handle.net/2097/35326.

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Master of Science<br>Department of Statistics<br>Christopher Vahl<br>Sample size estimation is generally based on assumptions made during the planning stage of a clinical trial. Often, there is limited information available to estimate the initial sample size. This may result in a poor estimate. For instance, an insufficient sample size may not have the capability to produce statistically significant results, while an over-sized study will lead to a waste of resources or even ethical issues in that too many patients are exposed to potentially ineffective treatments. Therefore, an interim analysis in the middle of a trial may be worthwhile to assure that the significance level is at the nominal level and/or the power is adequate to detect a meaningful treatment difference. In this report, the impact of sample size re-estimation on the type I error rate for the continuous end-point in a clinical trial with two treatments is evaluated through a simulation study. Two sample size estimation methods are taken into consideration: blinded and partially unblinded. For the blinded method, all collected data for two groups are used to estimate the variance, while only data from the control group are used to re-estimate the sample size for the partially unblinded method. The simulation study is designed with different combinations of assumed variance, assumed difference in treatment means, and re-estimation methods. The end-point is assumed to follow normal distribution and the variance for both groups are assumed to be identical. In addition, equal sample size is required for each group. According to the simulation results, the type I error rates are preserved for all settings.
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Books on the topic "Sample size estimation"

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(Jessica), Blaylock J., Rago Paul J, and Northeast Fisheries Science Center (U.S.), eds. River herring discard estimation, precision, and sample size analysis. U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Northeast Fisheries Science Center, 2009.

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Schreuder, Hans T. Annual design-based estimation for the annualized inventories of forest inventory and analysis: Sample size determination. U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2000.

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Schreuder, Hans T. Annual design-based estimation for the annualized inventories of forest inventory and analysis: Sample size determination. U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2000.

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S, Lin Jin-Mann, Teply John, and Rocky Mountain Research Station (Fort Collins, Colo.), eds. Annual design-based estimation for the annualized inventories of forest inventory and analysis: Sample size determination. U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2000.

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Schreuder, Hans T. Annual design-based estimation for the annualized inventories of forest inventory and analysis: Sample size determination. U.S. Dept. of Agriculture, Forest Service, Rocky Mountain Research Station, 2000.

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Christine, Bachrach, National Survey of Family Growth (U.S.), and National Center for Health Statistics (U.S.), eds. National survey of family growth, cycle III: Sample design, weighting, and variance estimation : this report describes the procedures used to select the sample. U.S. Dept. of Health and Human Services, Public Health Service, National Center for Health Statistics, 1985.

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J, Potter Frank, ed. Sample design, sampling weights, imputation, and variance estimation in the 1995 National Survey of Family Growth. National Center for Health Statistics, Centers for Disease Control and Prevention, U.S. Dept. of Health and Human Services, 1998.

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author, Blaylock J. (Jessica), Rago Paul J. author, Shield G. author, and Northeast Fisheries Science Center (U.S.), eds. 2012 discard estimation, precision, and sample size analyses for 14 federally managed species groups in the northeast region. US Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Northeast Fisheries Science Center, 2012.

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Phillips, A. J. Problems in population identification when sample membership is unknown and in estimation of the size of a heterogeneous population. University of Birmingham, 1986.

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Lenz, Sylvia Tamara. Nonnegative variance estimation of the Hotvitz-Thompson estimator for samples of fixed size when sampling without replacement. [s.n.], 1989.

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Book chapters on the topic "Sample size estimation"

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Yadav, Shakti Kumar, Sompal Singh, and Ruchika Gupta. "Sample Size Estimation." In Biomedical Statistics. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9294-9_18.

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Turner, J. Rick. "Sample Size Estimation." In Encyclopedia of Behavioral Medicine. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39903-0_1072.

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Turner, J. Rick. "Sample Size Estimation." In Encyclopedia of Behavioral Medicine. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_1072.

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Nahler, Gerhard. "sample size estimation." In Dictionary of Pharmaceutical Medicine. Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_1261.

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Turner, J. Rick. "Sample Size Estimation." In New Drug Development. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6418-2_11.

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Mitra, Amal K. "Sample Size Estimation." In Statistical Approaches for Epidemiology. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-41784-9_17.

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Chakraborty, Dev P. "Sample size estimation." In Observer Performance Methods for Diagnostic Imaging. CRC Press, 2017. http://dx.doi.org/10.1201/9781351228190-11.

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Almonroeder, Thomas Gus. "Sample size estimation." In Advanced Statistics for Physical and Occupational Therapy. Routledge, 2022. http://dx.doi.org/10.4324/9781003179757-14.

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Mütze, Tobias, and Tim Friede. "Sample Size Re-Estimation." In Handbook of Statistical Methods for Randomized Controlled Trials. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781315119694-16.

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Hennink, Monique. "Teaching Qualitative Sample Size Estimation." In The Handbook of Teaching Qualitative and Mixed Research Methods. Routledge, 2023. http://dx.doi.org/10.4324/9781003213277-6.

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Conference papers on the topic "Sample size estimation"

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Sengar, Namrata, and Koushal Shringi. "Comparative Study of Four Transparent Hydrophobic Coatings for Water Saving Potential in Dust Cleaning Context of Solar Power Plants." In 22nd ISME International Conference on Recent Advances in Mechanical Engineering for Sustainable Development. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-bxmjm9.

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Dust deposition may reduce the yield of the PV panels from 10-50% depending upon the amount of dust deposited, particle size and nature. To prevent loss of efficiency of power plant, cleaning of PV panels is generally required in one-two weeks and in summers during dust storms cleaning frequency needs to be increased. Generally, for cleaning de-ionised water is recommended which adds to the cost and even availability of ordinary water for cleaning is a problem with water scarce regions. In the world, most of the high solar potential sites which are ideal for solar PV power plant installation lie in water scarce regions. The attractive locations for solar energy in Asia and Sub-Saharan Africa are water stressed. Therefore, it becomes important to devise methods to reduce the water consumption in cleaning of solar PV panels in solar power plants. There are studies going on several methods, one such option is use of transparent hydrophobic coatings on the solar panel surface to reduce dust deposition and water used in cleaning. The present work is a step in the direction of estimation of reduction of water consumption with the use of transparent hydrophobic coatings. The present paper discusses the characteristics of dust particles deposited on the solar power plant at University of Kota, Kota, India location and compares the water use amount in cleaning dust on five glass samples. The five samples consist of four different transparent hydrophobic coatings available in market and one is the reference uncoated glass sample. Tests have been done and reported for transparency, dust deposition and water use amount in cleaning for the five samples. On the basis of the comparative study, the amount of water saving potential is estimated for solar power plants. The challenges in use of hydrophobic coatings have been discussed and scope for future work in this field has been examined.
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Chen, Weijie, Zhipeng Huang, Frank W. Samuelson, and Lucas Tcheuko. "Adaptive sample size re-estimation in MRMC studies." In Image Perception, Observer Performance, and Technology Assessment, edited by Robert M. Nishikawa and Frank W. Samuelson. SPIE, 2019. http://dx.doi.org/10.1117/12.2513646.

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Bradley, A. P., and I. D. Longstaff. "Sample size estimation using the receiver operating characteristic curve." In Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1333794.

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Salazar, Addisson, Luis Vergara, and Alberto Gonzalez. "A Training Sample Size Estimation for the Bayes Classifier." In 2023 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2023. http://dx.doi.org/10.1109/csci62032.2023.00049.

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Luo, Ruihao, Shuxia Guo, and Thomas Bocklitz. "Sample Size Estimation of Transfer Learning for Colorectal Cancer Detection." In 13th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012449500003654.

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Manlin Xiao, Xin Qi, and Ping Wei. "Parametric direction of arrival estimation in the small sample-size case." In 2010 International Conference on Image Analysis and Signal Processing. IEEE, 2010. http://dx.doi.org/10.1109/iasp.2010.5476063.

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Zhou, Li-na, Rui Huang, Xian-hua Li, and Ling Chen. "Semi-Supervised Covariance Estimation Using Clustering for Small Sample Size Problem." In 2009 1st International Conference on Information Science and Engineering (ICISE 2009). IEEE, 2009. http://dx.doi.org/10.1109/icise.2009.1056.

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Guiyan Jiang, Longhui Gang, and Zhili Cai. "Impact of Probe Vehicles Sample Size on Link Travel Time Estimation." In 2006 IEEE Intelligent Transportation Systems Conference. IEEE, 2006. http://dx.doi.org/10.1109/itsc.2006.1706791.

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Zhang, Yani, Tianyi Liu, and Marius Pesavento. "Direction-of-Arrival Estimation for Correlated Sources and Low Sample Size." In 2023 31st European Signal Processing Conference (EUSIPCO). IEEE, 2023. http://dx.doi.org/10.23919/eusipco58844.2023.10290019.

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Zeng, Minhui, John Douglas Hunt, and Ming Zhong. "Effect of within-sample choice distribution and sample size on the estimation accuracy of logit model." In 2015 International Conference on Transportation Information and Safety (ICTIS). IEEE, 2015. http://dx.doi.org/10.1109/ictis.2015.7232160.

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Reports on the topic "Sample size estimation"

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Ibarburu, Maro, James B. Kliebenstein, and Brent M. Hueth. Estimation of the Necessary Sample Size for Predicting Meat Quality Characteristics for Producers. Iowa State University, 2007. http://dx.doi.org/10.31274/ans_air-180814-630.

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Schreuder, Hans T., Jin-Mann S. Lin, and John Teply. Annual design-based estimation for the annualized inventories of forest inventory and analysis: sample size determination. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, 2000. http://dx.doi.org/10.2737/rmrs-gtr-66.

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Villoria, Nelson B. Estimation of Missing Intra-African Trade. GTAP Research Memoranda, 2008. http://dx.doi.org/10.21642/gtap.rm12.

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Missing trade is defined as the exports and imports that may have taken place between two potential trading partners, but which are unknown to the researcher because neither partner reported them to the United Nation’s COMTRADE, the official global repository of trade statistics. In a comprehensive sample of African countries, over 40% of the potential trade flows fit this definition. For a continent whose trade integration remains an important avenue for development, this lack of information hinders the analysis of policy mechanisms -- such as the Economic Partnership Agreements with the EU -- that influence intra-regional trade patterns. This paper estimates the likely magnitude of the missing trade by modeling the manufacturing trade data in the GTAP Data Base using a gravity approach. The gravity approach employed here relates bilateral trade to country size, distance, and other trade costs while explicitly considering that high fixed costs can totally inhibit trade. This last feature provides an adequate framework to explain the numerous zero-valued flows that characterize intra-African trade. The predicted missing exports are valued at approximately 300 million USD. The incidence of missing trade is highest in the lowest income countries of Central and West Africa.
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Espino, Emilio, and Martín González Rozada. Automatic Stabilization and Fiscal Policy: Some Quantitative Implications for Latin America and the Caribbean. Inter-American Development Bank, 2012. http://dx.doi.org/10.18235/0011425.

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This paper provides an estimation of the size of income and demand automatic stabilizers in a representative sample of Latin American and Caribbean (LAC) countries. The authors find that when a negative unemployment shock hits the economy, the size of income and demand automatic stabilizers coefficients is much smaller than the size of these coefficients in Europe and the United States. This evidence suggests that there is room for policies that can enlarge the absorption by these coefficients as a way to contribute to macroeconomic stability in LAC countries. The paper analyzes four policies affecting the income stabilization coefficient and two others affecting directly the demand stabilization coefficient. The main results suggest that changing the minimum tax exemption and its progressiveness using the tax structure of middle-income countries outside the LAC region is the best option to enlarge the size of the income and demand stabilization coefficients, and in this way to reduce the need of discretionary fiscal policies in theregion.
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Blanco, Roberto, Elena Fernández, Miguel García-Posada, and Sergio Mayordomo. An estimation of the default probabilities of Spanish non-financial corporations and their application to evaluate public policies. Banco de España, 2023. http://dx.doi.org/10.53479/33512.

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We model the one-year ahead probability for default of Spanish non-financial corporations using data for the period 1996-2019. While most previous literature considers that a firm is in default if it files for bankruptcy, we define default as having non-performing loans during at least three months of a given year. This broader definition allows us to predict firms’ financial distress at an earlier stage that cannot generally be observed by researchers, before their financial conditions become too severe and they have to file for bankruptcy or engage in private workouts with their creditors. We estimate, by means of logistic regressions, both a general model that uses all the firms in the sample and six models for different size-sector combinations. The selected explanatory variables are five accounting ratios, which summarise firms’ creditworthiness, and the growth rate of aggregate credit to non-financial corporations, to take into account the role of credit availability in mitigating the risk of default. Finally, we carry out two applications of our prediction models: we construct credit rating transition matrices and evaluate a programme implemented by the Spanish government to provide direct aid to firms severely affected by the COVID-19 crisis.
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Magdalinos, Tassos, and Katerina Petrova. Uniform Inference with General Autoregressive Processes. Federal Reserve Bank of New York, 2025. https://doi.org/10.59576/sr.1151.

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A unified theory of estimation and inference is developed for an autoregressive process with root in (-∞, ∞) that includes the stationary, local-to-unity, explosive and all intermediate regions. The discontinuity of the limit distribution of the t-statistic outside the stationary region and its dependence on the distribution of the innovations in the explosive regions (-∞, -1) ∪ (1, ∞) are addressed simultaneously. A novel estimation procedure, based on a data-driven combination of a near-stationary and a mildly explosive artificially constructed instrument, delivers mixed-Gaussian limit theory and gives rise to an asymptotically standard normal t-statistic across all autoregressive regions. The resulting hypothesis tests and confidence intervals are shown to have correct asymptotic size (uniformly over the space of autoregressive parameters and the space of innovation distribution functions) in autoregressive, predictive regression and local projection models, thereby establishing a general and unified framework for inference with autoregressive processes. Extensive Monte Carlo simulation shows that the proposed methodology exhibits very good finite sample properties over the entire autoregressive parameter space (-∞, ∞) and compares favorably to existing methods within their parametric (-1, 1] validity range. We demonstrate how our procedure can be used to construct valid confidence intervals in standard epidemiological models as well as to test in real-time for speculative bubbles in the price of the Magnificent Seven tech stocks.
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Gutiérrez Fernández, Emilio, and Adrian Rubli. Challenges for Measuring the LGBT+ Population and Homophobia in Mexico. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0004747.

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We contribute to understanding the challenges for estimating the size of the LGBTQ population and discriminatory sentiment against it by surveying 10,003 individuals, whom we randomize into a direct question or an Item Count Technique (ICT) elicitation group. The fractions of the population that self-identify as LGBTQ, that reports having had same-sex sexual experiences, and that has felt same-sex attraction are higher for our sample than those obtained from government surveys. However, the difference between estimates recovered from our direct questions and through the ICT does not always have the expected sign. The negative relationship between age and self-identifying as non-heterosexual is present both in the government survey and in our direct question sample but vanishes when measured with the ICT. The positive correlation between age and homophobic sentiment is present across samples and elicitation techniques. We find no significant variation in all measures for formal vs informal workers.
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Shivakumar, Pranavkumar, Kanika Gupta, Antonio Bobet, Boonam Shin, and Peter J. Becker. Estimating Strength from Stiffness for Chemically Treated Soils. Purdue University, 2022. http://dx.doi.org/10.5703/1288284317383.

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The central theme of this study is to identify strength-stiffness correlations for chemically treated subgrade soils in Indiana. This was done by conducting Unconfined Compression (UC) Tests and Resilient Modulus Tests for soils collected at three different sites—US-31, SR-37, and I-65. At each site, soil samples were obtained from 11 locations at 30 ft spacing. The soils were treated in the laboratory with cement, using the same proportions used for construction, and cured for 7 and 28 days before testing. Results from the UC tests were compared with the resilient modulus results that were available. No direct correlation was found between resilient modulus and UCS parameters for the soils investigated in this study. A brief statistical analysis of the results was conducted, and a simple linear regression model involving the soil characteristics (plasticity index, optimum moisture content and maximum dry density) along with UCS and resilient modulus parameters was proposed.
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Bouezmarni, Taoufik, Mohamed Doukali, and Abderrahim Taamouti. Copula-based estimation of health concentration curves with an application to COVID-19. CIRANO, 2022. http://dx.doi.org/10.54932/mtkj3339.

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COVID-19 has created an unprecedented global health crisis that caused millions of infections and deaths worldwide. Many, however, argue that pre-existing social inequalities have led to inequalities in infection and death rates across social classes, with the most-deprived classes are worst hit. In this paper, we derive semi/non-parametric estimators of Health Concentration Curve (HC) that can quantify inequalities in COVID-19 infections and deaths and help identify the social classes that are most at risk of infection and dying from the virus. We express HC in terms of copula function that we use to build our estimators of HC. For the semi-parametric estimator, a parametric copula is used to model the dependence between health and socio-economic variables. The copula function is estimated using maximum pseudo-likelihood estimator after replacing the cumulative distribution of health variable by its empirical analogue. For the non-parametric estimator, we replace the copula function by a Bernstein copula estimator. Furthermore, we use the above estimators of HC to derive copula-based estimators of health Gini coeffcient. We establish the consistency and the asymptotic normality of HC’s estimators. Using different data-generating processes and sample sizes, a Monte-Carlo simulation exercise shows that the semiparametric estimator outperforms the smoothed nonparametric estimator, and that the latter does better than the empirical estimator in terms of Integrated Mean Squared Error. Finally, we run an extensive empirical study to illustrate the importance of HC’s estimators for investigating inequality in COVID-19 infections and deaths in the U.S. The empirical results show that the inequalities in state’s socio-economic variables like poverty, race/ethnicity, and economic prosperity are behind the observed inequalities in the U.S.’s COVID-19 infections and deaths.
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Fourqurean, James, Johannes Krause, Juan González-Corredor, Tom Frankovich, and Justin Campbell. Caricas Partner's Practical Field and Laboratory Guide. Florida International University, 2024. http://dx.doi.org/10.25148/merc_fac.2024.32.

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This field and laboratory guide describes the field and laboratory methods used to characterize blue carbon in seagrass meadows. It was developed for the Caribbean Carbon Accounting in Seagrass project and describes the protocols and methods used by the network. In brief, at each project site, seagrass abundance, species composition, canopy height, and sediment type were assessed at sixteen 0.25 m2 quadrats placed at random locations within the site. Eight 20 cm diameter cores were taken to assess seagrass biomass, shoot density, and to provide the material for assessing seagrass carbon and nutrient content. All seagrasses within each of the eight cores were separated by species and tissue type, washed and scraped to remove epiphytes, then dried and weighed. A piston core of uncompressed soils was retrieved, to a depth of 1 m or until refusal. Cores were subsampled at 5 cm depth intervals using small subcorers. All subcores were weighed wet to permit the calculation of porosity and soil dry bulk density. Seagrass tissue and sediment samples were oven-dried at 60°C, and dry weight recorded. Finally, samples were analyzed in the laboratory for determination of Loss on Ignition, total carbon content, inorganic carbon content, organic carbon content, and carbon and nitrogen content as well as stable isotope ratios. The resulting data allow for the estimation of seagrass organic carbon stocks as well as nutrient and carbonate stocks in biomass and sediment, their relationship with environmental covariates, and the contribution of seagrass material to carbon stocks.
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