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Books on the topic 'Sample size estimation'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 June 2025

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1

(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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Sawyer, Richard. Determining minimum sample sizes for estimating prediction equations for college freshman grade average. American College Testing Program, 1987.

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12

R, Herring J., and Geological Survey (U.S.), eds. Size analysis, visual estimation of phosphate and other minerals, and preliminary estimation of recoverable phosphate in size fractions of sediment samples from drillholes GAT-90, Tybee Island, and GAS-90-2, Skidaway Island, Georgia. U.S. Dept. of the Interior, U.S. Geological Survey, 1991.

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13

Tang, K. Linda. A study of the use of collateral statistical information in attempting to reduce TOEFL IRT item parameter estimation sample sizes. Educational Testing Service, 2001.

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14

Lui, Kung-Jong. Crossover Designs: Testing, Estimation, and Sample Size. Wiley & Sons, Incorporated, John, 2016.

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15

Lui, Kung-Jong. Crossover Designs: Testing, Estimation, and Sample Size. Wiley & Sons, Limited, John, 2016.

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16

Lui, Kung-Jong. Crossover Designs: Testing, Estimation, and Sample Size. Wiley & Sons, Incorporated, John, 2016.

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17

Lui, Kung-Jong. Crossover Designs: Testing, Estimation, and Sample Size. Wiley & Sons, Incorporated, John, 2016.

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18

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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19

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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20

Sampling Techniques: Methods and Applications. Nova Science Publishers Inc, 2018.

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21

Estimating Samples Sizes in Clinical Trials. Chapman & Hall/CRC, 2008.

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22

Gelman, Andrew, and Deborah Nolan. Statistical inference. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0009.

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This chapter begins with a very successful demonstration that illustrates many of the general principles of statistical inference, including estimation, bias, and the concept of the sampling distribution. Students each take a “random” sample of different size candies, weigh them, and estimate the total weight of all candies. Then various demonstrations and examples are presented that take the students on the transition from probability to hypothesis testing, confidence intervals, and more advanced concepts such as statistical power and multiple comparisons. These activities include use an inflatable globe, short-term memory test, first digits of street addresses, and simulated student IQs.

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23

Laver, Michael, and Ernest Sergenti. Systematically Interrogating Agent-Based Models. Princeton University Press, 2017. http://dx.doi.org/10.23943/princeton/9780691139036.003.0004.

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This chapter develops the methods for designing, executing, and analyzing large suites of computer simulations that generate stable and replicable results. It starts with a discussion of the different methods of experimental design, such as grid sweeping and Monte Carlo parameterization. Next, it demonstrates how to calculate mean estimates of output variables of interest. It does so by first discussing stochastic processes, Markov Chain representations, and model burn-in. It focuses on three stochastic process representations: nonergodic deterministic processes that converge on a single state; nondeterministic stochastic processes for which a time average provides a representative estimate of the output variables; and nondeterministic stochastic processes for which a time average does not provide a representative estimate of the output variables. The estimation strategy employed depends on which stochastic process the simulation follows. Lastly, the chapter presents a set of diagnostic checks used to establish an appropriate sample size for the estimation of the means.

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24

Ślusarski, Marek. Metody i modele oceny jakości danych przestrzennych. Publishing House of the University of Agriculture in Krakow, 2017. http://dx.doi.org/10.15576/978-83-66602-30-4.

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The quality of data collected in official spatial databases is crucial in making strategic decisions as well as in the implementation of planning and design works. Awareness of the level of the quality of these data is also important for individual users of official spatial data. The author presents methods and models of description and evaluation of the quality of spatial data collected in public registers. Data describing the space in the highest degree of detail, which are collected in three databases: land and buildings registry (EGiB), geodetic registry of the land infrastructure network (GESUT) and in database of topographic objects (BDOT500) were analyzed. The results of the research concerned selected aspects of activities in terms of the spatial data quality. These activities include: the assessment of the accuracy of data collected in official spatial databases; determination of the uncertainty of the area of registry parcels, analysis of the risk of damage to the underground infrastructure network due to the quality of spatial data, construction of the quality model of data collected in official databases and visualization of the phenomenon of uncertainty in spatial data. The evaluation of the accuracy of data collected in official, large-scale spatial databases was based on a representative sample of data. The test sample was a set of deviations of coordinates with three variables dX, dY and Dl – deviations from the X and Y coordinates and the length of the point offset vector of the test sample in relation to its position recognized as a faultless. The compatibility of empirical data accuracy distributions with models (theoretical distributions of random variables) was investigated and also the accuracy of the spatial data has been assessed by means of the methods resistant to the outliers. In the process of determination of the accuracy of spatial data collected in public registers, the author’s solution was used – resistant method of the relative frequency. Weight functions, which modify (to varying degree) the sizes of the vectors Dl – the lengths of the points offset vector of the test sample in relation to their position recognized as a faultless were proposed. From the scope of the uncertainty of estimation of the area of registry parcels the impact of the errors of the geodetic network points was determined (points of reference and of the higher class networks) and the effect of the correlation between the coordinates of the same point on the accuracy of the determined plot area. The scope of the correction was determined (in EGiB database) of the plots area, calculated on the basis of re-measurements, performed using equivalent techniques (in terms of accuracy). The analysis of the risk of damage to the underground infrastructure network due to the low quality of spatial data is another research topic presented in the paper. Three main factors have been identified that influence the value of this risk: incompleteness of spatial data sets and insufficient accuracy of determination of the horizontal and vertical position of underground infrastructure. A method for estimation of the project risk has been developed (quantitative and qualitative) and the author’s risk estimation technique, based on the idea of fuzzy logic was proposed. Maps (2D and 3D) of the risk of damage to the underground infrastructure network were developed in the form of large-scale thematic maps, presenting the design risk in qualitative and quantitative form. The data quality model is a set of rules used to describe the quality of these data sets. The model that has been proposed defines a standardized approach for assessing and reporting the quality of EGiB, GESUT and BDOT500 spatial data bases. Quantitative and qualitative rules (automatic, office and field) of data sets control were defined. The minimum sample size and the number of eligible nonconformities in random samples were determined. The data quality elements were described using the following descriptors: range, measure, result, and type and unit of value. Data quality studies were performed according to the users needs. The values of impact weights were determined by the hierarchical analytical process method (AHP). The harmonization of conceptual models of EGiB, GESUT and BDOT500 databases with BDOT10k database was analysed too. It was found that the downloading and supplying of the information in BDOT10k creation and update processes from the analyzed registers are limited. An effective approach to providing spatial data sets users with information concerning data uncertainty are cartographic visualization techniques. Based on the author’s own experience and research works on the quality of official spatial database data examination, the set of methods for visualization of the uncertainty of data bases EGiB, GESUT and BDOT500 was defined. This set includes visualization techniques designed to present three types of uncertainty: location, attribute values and time. Uncertainty of the position was defined (for surface, line, and point objects) using several (three to five) visual variables. Uncertainty of attribute values and time uncertainty, describing (for example) completeness or timeliness of sets, are presented by means of three graphical variables. The research problems presented in the paper are of cognitive and application importance. They indicate on the possibility of effective evaluation of the quality of spatial data collected in public registers and may be an important element of the expert system.

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25

Gelman, Andrew, and Deborah Nolan. Student activities in survey sampling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198785699.003.0018.

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This chapter outlines some of our more effective demonstrations for teaching sampling. Part I of this book contains many activities related to sampling that we also use in our more advanced courses on the subject (e.g., see chapter 6 for an activity on estimating family size, and chapter 9 for a candy weighing activity). This chapter describes additional student activities that we have developed for the advanced undergraduate survey sampling class. These include provocative questionnaires to demonstrate question bias and statistical literacy packets for dissecting news stories about surveys. In addition, this chapter contains sample handouts used for teaching particular topics, techniques for encouraging student participation, and materials to organize student projects on complex surveys.

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26

Miksza, Peter, and Kenneth Elpus. Inferential Analysis. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199391905.003.0005.

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Researchers often employ statistical techniques to test hypotheses and to express the relative certainty they have when making a claim about how statistics derived from their sample data might be representative of population parameters. This chapter illustrates the logic underlying inferential statistical tests. Inferential analyses involves a set of tools that music education researchers can use when posing scientific questions and seeking to refute their hypotheses. The chapter describes techniques that can be used for testing hypotheses and estimating population parameters on the basis of sample data. In doing so, the chapter emphasizes basic approaches to null hypothesis significance testing, interpreting effect sizes, and building confidence intervals. The chapter also provides a brief critique of null hypothesis significance testing as a tradition.

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27

Strand, Vibeke, Jeremy Sokolove, and Alvina D. Chu. Design of clinical trials in rheumatology. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0030.

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Development of new therapies for rheumatic diseases requires a series of randomized controlled trials (RCTs) progressing from phase 1, 'first-in-human' to generate initial safety, pharmacokinetic (PK) and pharmacodynamic (PD) data; to phase 2, proof of concept for efficacy with safety and PK/PD data; and phase 3, designed to demonstrate definitive efficacy and safety to support regulatory approval. Important aspects of RCT designs include sample size estimations, treatment allocation, rescue, blinding, and statistical analyses of prespecified endpoints to preserve trial integrity. Over the past 15 years, significant progress has been made in the design of RCTs in rheumatoid arthritis (RA). Similarly, development and validation of composite outcome measures in psoriatic arthritis, ankylosing spondylitis, gout, and osteoarthritis have furthered trial design and treatment approvals. RCTs in systemic lupus erythematosus and other multisystem, heterogeneous diseases pose more challenges. Trial design will continue to evolve as promising therapies are introduced into the clinic.

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28

Strand, Vibeke, Jeremy Sokolove, and Alvina D. Chu. Design of clinical trials in rheumatology. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199642489.003.0030_update_001.

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Development of new therapies for rheumatic diseases requires a series of randomized controlled trials (RCTs) progressing from phase 1, ’first-in-human’ to generate initial safety, pharmacokinetic (PK) and pharmacodynamic (PD) data; to phase 2, proof of concept for efficacy with safety and PK/PD data; and phase 3, designed to demonstrate definitive efficacy and safety to support regulatory approval. Important aspects of RCT designs include sample size estimations, treatment allocation, rescue, blinding, and statistical analyses of prespecified endpoints to preserve trial integrity. Over the past 15 years, significant progress has been made in the design of RCTs in rheumatoid arthritis (RA). Similarly, development and validation of composite outcome measures in psoriatic arthritis, ankylosing spondylitis, gout, and osteoarthritis have furthered trial design and treatment approvals. RCTs in systemic lupus erythematosus and other multisystem, heterogeneous diseases pose more challenges. Trial design will continue to evolve as promising therapies are introduced into the clinic.

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29

Strand, Vibeke, Jeremy Sokolove, and Alvina D. Chu. Design of clinical trials in rheumatology. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199642489.003.0030_update_002.

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Abstract:

Development of new therapies for rheumatic diseases requires a series of randomized controlled trials (RCTs) progressing from phase 1, ’first-in-human’ to generate initial safety, pharmacokinetic (PK) and pharmacodynamic (PD) data; to phase 2, proof of concept for efficacy with safety and PK/PD data; and phase 3, designed to demonstrate definitive efficacy and safety to support regulatory approval. Important aspects of RCT designs include sample size estimations, treatment allocation, rescue, blinding, and statistical analyses of prespecified endpoints to preserve trial integrity. Over the past 15 years, significant progress has been made in the design of RCTs in rheumatoid arthritis (RA). Similarly, development and validation of composite outcome measures in psoriatic arthritis, ankylosing spondylitis, gout, and osteoarthritis have furthered trial design and treatment approvals. RCTs in systemic lupus erythematosus and other multisystem, heterogeneous diseases pose more challenges. Trial design will continue to evolve as promising therapies are introduced into the clinic.

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30

Walsh, Bruce, and Michael Lynch. Using Molecular Data to Detect Selection: Signatures from Multiple Historical Events. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0010.

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This chapter examines the search for a pattern of repetitive adaptive substitutions over evolutionary time. In contrast with the previous chapter, only a modest number of tests toward this aim have been proposed. The HKA and McDonald-Kreitman tests contrast the polymorphism to divergence ratio between different genomic classes (such as different genes or silent versus replacement sites within the same gene). These approaches can detect an excess of substitutions, which allows one to estimate the fraction of adaptive sites. This chapter reviews the empirical data on estimates of this fraction and discusses some of the sources of bias it its estimation. Over an even longer time scale, one can contrast the rate of change of sites in a sequence over a phylogeny. These tests require a rather special type of selection, wherein the same specific site (usually a codon) experiences multiple adaptive substitutions over a phylogeny, such as might occur in arms-race genes.

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