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Books on the topic 'Hypothesis sampling'

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Author: Grafiati

Published: 10 December 2022

Last updated: 29 January 2023

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1

Permutation, parametric and bootstrap tests of hypotheses. 3rd ed. New York: Springer, 2005.

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2

Lin, Nancy Pei-ching. A new approach to sample size determination of replicated Latin square designs and analysis of multiple comparison procedures. [Tʻai-pei shih: Ching sheng wen wu kung ying kung ssu, 1985.

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3

Siegmund, David. Sequential analysis: Tests and confidence intervals. New York: Springer-Verlag, 2010.

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4

Loether, Herman J. Descriptive and inferential statistics: An introduction. 3rd ed. Boston: Allyn and Bacon, 1988.

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5

G, McTavish Donald, ed. Descriptive and inferential statistics: An introduction. 4th ed. Boston: Allyn and Bacon, 1993.

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6

Böhning, Dankmar. On minimizing chi-square distances under the hypothesis of homogeneity of independence for a two-way contingency table. Osnabrück: Fachbereich Psychologie, Universität Osnabrück, 1985.

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7

Odeh, Robert E. Sample size choice: Charts for experiments with linear models. 2nd ed. New York, N.Y: M. Dekker, 1991.

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8

Randomization tests. 2nd ed. New York: M. Dekker, 1987.

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9

Patrick, Onghena, ed. Randomization tests. 4th ed. Boca Raton, Fla: Taylor & Francis, 2007.

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10

Edgington, Eugene S. Randomization tests. 3rd ed. New York: M. Dekker, 1995.

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11

Petrucci, Alessandra, and Rosanna Verde, eds. SIS 2017. Statistics and Data Science: new challenges, new generations. Florence: Firenze University Press, 2017. http://dx.doi.org/10.36253/978-88-6453-521-0.

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The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data.

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12

McDonald, Trent. Analysis of finite population surveys: Sample size and testing considerations. 1996.

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13

Permutation, Parametric, and Bootstrap Tests of Hypotheses. Springer London, Limited, 2006.

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14

Beran, Rudolf, and Gilles R. Ducharme. Asymptotic Theory for Bootstrap Methods in Statistics. Centre De Recherches Mathematiques, 1991.

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15

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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16

Fox, Martin, and Robert E. Odeh. Sample Size Choice: Charts for Experiments with Linear Models, Second Edition. Taylor & Francis Group, 2020.

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17

Fox, Martin, and Robert E. Odeh. Sample Size Choice: Charts for Experiments with Linear Models, Second Edition. Taylor & Francis Group, 2020.

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18

Fox, Martin, and Robert E. Odeh. Sample Size Choice: Charts for Experiments with Linear Models, Second Edition. Taylor & Francis Group, 2020.

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19

Fox, Martin, and Robert E. Odeh. Sample Size Choice. Taylor & Francis Group, 2019.

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20

Fox, Martin, and Robert E. Odeh. Sample Size Choice: Charts for Experiments with Linear Models, Second Edition. Taylor & Francis Group, 2020.

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21

Edgington, Eugene, and Patrick Onghena. Randomization Tests. Taylor & Francis Group, 2007.

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22

Edgington, Eugene, and Patrick Onghena. Randomization Tests, Fourth Edition (Statistics: a Series of Textbooks and Monographs). 4th ed. Chapman & Hall/CRC, 2007.

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23

Randomization Tests. Taylor & Francis Group, 2007.

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24

Cheng, Russell. Bootstrap Analysis. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198505044.003.0004.

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Parametric bootstrapping (BS) provides an attractive alternative, both theoretically and numerically, to asymptotic theory for estimating sampling distributions. This chapter summarizes its use not only for calculating confidence intervals for estimated parameters and functions of parameters, but also to obtain log-likelihood-based confidence regions from which confidence bands for cumulative distribution and regression functions can be obtained. All such BS calculations are very easy to implement. Details are also given for calculating critical values of EDF statistics used in goodness-of-fit (GoF) tests, such as the Anderson-Darling A2 statistic whose null distribution is otherwise difficult to obtain, as it varies with different null hypotheses. A simple proof is given showing that the parametric BS is probabilistically exact for location-scale models. A formal regression lack-of-fit test employing parametric BS is given that can be used even when the regression data has no replications. Two real data examples are given.

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25

Burns, Tom, and Mike Firn. Research and development. Edited by Tom Burns and Mike Firn. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198754237.003.0029.

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This chapter covers the spectrum of routine monitoring, audit, service evaluation, and formal research. Routine monitoring is an essential task for all mental health professionals, and techniques to make it more palatable are explored, including using routine data for clinical supervision and monitoring team targets. Regular audit is described as an essential tool for logical service development and quality improvement. In the discussion of research, the importance of choosing the correct methodology and of paying attention to detail are stressed. In community psychiatry, sampling bias, regression to the mean, and the Hawthorne effect pose important risks. The hierarchy of research methods is outlined with randomized controlled trials (RCTs) at the top, preferably with either single- or double-blinding. Careful statistics and systematic reviews support evidence-based practice. In addition to experimental quantitative trials, there is a place for cohort and case control trials, as well as for qualitative trials to generate hypotheses.

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