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Published: 8 September 2023

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1

Diebold, Francis X. Regime switching with time-varying transition probabilities. Philadelphia: Federal Reserve Bank of Philadelphia, Economic Research Division, 1993.

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2

Gurao, Dr Rajendra G. Sampling Methods. Kanpur, India: Chandralok Prakashan, 2015.

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3

1931-, MacNeill Ian B., and Umphrey Gary J. 1953-, eds. Applied probability, stochastic processes, and sampling theory. Dordrecht: D. Reidel, 1987.

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4

Wright, Tommy. Exact confidence bounds when sampling from small finite universes: An easy reference based on the hypergeometric distribution. Berlin: Springer-Verlag, 1991.

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5

Introduction to empirical processes and semiparametric inference. New York: Springer, 2008.

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6

Gupta, A. K. Theory of sample surveys. New Jersey: World Scientific, 2011.

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7

G, Meeden, ed. Bayesian methods for finite population sampling. London: Chapman & Hall, 1997.

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8

Resampling: The new statistics. 2nd ed. Arlington, VA: Resampling Stats, 1999.

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9

R.C. Bose Symposium (1988 New Delhi). Probability, statistics and design of experiments. New Delhi: Wiley Eastern, 1990.

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10

Evans, Michael J. Monte Carlo computation of marginal posterior qualities. Toronto: University of Toronto, Dept. of Statistics, 1988.

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11

Miliken, George A. Analysis of messy data. New York: Chapman & Hall, 1992.

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12

A, Levin Bruce, and Paik Myunghee Cho, eds. Statistical methods for rates and proportions. 3rd ed. Hoboken, N.J: J. Wiley, 2003.

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13

Gail, Burrill, and National Council of Teachers of Mathematics., eds. Navigating through data analysis in grades 9-12. Reston, VA: National Council of Teachers of Mathematics, 2003.

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14

Zanella, Angelo, Benito V. Frosini, Umberto Magagnoli, and Giuseppe Boari. Studi in onore di Angelo Zanella. Milano: V&P università, 2002.

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15

P, Kroese Dirk, ed. Simulation and the monte carlo method. 2nd ed. Hoboken, N.J: John Wiley & Sons, 2008.

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16

Brewer, K. R. W., and M. Hanif. Sampling With Unequal Probabilities. Springer, 2013.

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17

Brewer, K. R. W., and M. Hanif. Sampling with Unequal Probabilities. Springer London, Limited, 2013.

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18

Ghosh, Malay. Bayesian Methods for Finite Population Sampling. CRC Press LLC, 2021.

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19

Ghosh, Malay. Bayesian Methods for Finite Population Sampling. CRC Press LLC, 2021.

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20

Ghosh, Malay. Bayesian Methods for Finite Population Sampling. CRC Press LLC, 2021.

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21

Advances in the Statistical Sciences: Applied Probability, Stochastic Processes, and Sampling Theory: Volume I of the Festschrift in Honor of Professor ... Ontario Series in Philosophy of Science). Springer, 1986.

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22

Wright, Tommy. Exact Confidence Bounds When Sampling from Small Finite Universes: An Easy Reference Based on the Hypergeometric Distribution. Springer London, Limited, 2012.

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23

Exact Confidence Bounds when Sampling from Small Finite Universes: An Easy Reference Based on the Hypergeometric Distribution. Springer, 2011.

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24

Kosorok, Michael R. Introduction to Empirical Processes and Semiparametric Inference. Springer New York, 2010.

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25

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

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26

Ghosh, Malay. Bayesian Methods for Finite Population Sampling. CRC Press LLC, 2021.

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27

Sampling threatened and endangered species with non-constant occurrence and detectability:: A sensitivity analysis of power when sampling low-occurrence populations with varying probability parameters. Arcata, California: Humboldt State University, 1999.

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28

Milliken, George A., and Dallas E. Johnson. Analysis of Messy Data, Volume I: Designed Experiments (Analysis of Messy Data). Chapman & Hall/CRC, 1993.

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29

Simon, Julian Lincoln. Resampling: The new statistics. Resampling Stats, 1995.

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30

The Theory Of Sample Surveys And Statistical Decisions. Pitam Pura, New Delhi, India: New India Publishing Agency, 2009.

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31

Kraberg, Alexandra, Katja Metfies, and Rowena Stern. Sampling, Preservation and Counting of Samples I: Phytoplankton. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199233267.003.0009.

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This chapter reviews phytoplankton sampling and analysis techniques, discussing them in light of their advantages and disadvantages. Different sampling methods have varying levels of precision and accuracy. This means that they affect the ways in which individual data sets can be interpreted, and methods therefore have to be kept consistent within time series to avoid creating artefacts. The discussions cover qualitative and semi-quantitative methods, quantitative methods, sample analysis, automated/semi-automated systems, and molecular methodologies. None of the methods are universally applicable but depend on the right set of tools and the scientific and financial context in which they are used. Molecular techniques hold great promise particularly for taxa that cannot be identified by routine microscopical techniques.

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32

Suess, Eric A., and Bruce E. Trumbo. Gibbs Sampling and Screening Tests: From Random Numbers to the Gibbs Sampler (Springer Texts in Statistics). Springer, 2006.

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33

Introduction to Probability Simulation and Gibbs Sampling with R. Springer London, Limited, 2010.

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34

Coolen, A. C. C., A. Annibale, and E. S. Roberts. Markov Chain Monte Carlo sampling of graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0006.

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This chapter looks at Markov Chain Monte Carlo techniques to generate hard- and soft-constrained exponential random graph ensembles. The essence is to define a Markov chain based on ergodic randomization moves acting on a network with transition probabilities which satisfy detailed balance. This is sufficient to ensure that the Markov chain will sample from the ensemble with the desired probabilities. This chapter studies several commonly seen randomization move sets and carefully defines acceptance probabilities for a range of different ensembles using both the Metropolis–Hastings and the Glauber prescription. Particular care is paid to describe and avoid the pitfalls that can occur in defining randomization moves for hard-constrained ensembles, and applying them without introducing inadvertent bias (i.e. defining and comparing protocols including switch-and-hold and mobility).

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35

Fleiss, Joseph L. Statistical Methods for Rates & Proportions. 2nd ed. Wiley-Interscience, 2000.

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36

Fleiss, Joseph L., Bruce Levin, and Myunghee Cho Paik. Statistical Methods for Rates and Proportions. Wiley & Sons Australia, Limited, John, 2001.

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37

Fleiss, Joseph L., Bruce Levin, and Myunghee Cho Paik. Statistical Methods for Rates and Proportions. Wiley & Sons, Incorporated, John, 2008.

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38

Fleiss, Joseph L., Bruce Levin, and Myunghee Cho Paik. Statistical Methods for Rates and Proportions. Wiley & Sons, Incorporated, John, 2004.

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39

Fleiss, Joseph L., Bruce Levin, and Myunghee Cho Paik. Statistical Methods for Rates and Proportions. Wiley & Sons, Incorporated, John, 2013.

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40

Living with Uncertainty. Cambridge University Press, 1993.

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41

Survey Weights. Taylor & Francis Group, 2018.

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42

Kroese, Dirk P., and Reuven Y. Rubinstein. Simulation and the Monte Carlo Method. Wiley & Sons, Incorporated, John, 2016.

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43

Rubinstein, Reuven Y. Simulation and the Monte Carlo Method. Wiley & Sons, Incorporated, John, 2009.

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44

Kroese, Dirk P., and Reuven Y. Rubinstein. Simulation and the Monte Carlo Method. Wiley & Sons, Incorporated, John, 2011.

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45

Kroese, Dirk P., and Reuven Y. Rubinstein. Simulation and the Monte Carlo Method. Wiley & Sons, Incorporated, John, 2016.

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46

Probability And Statistics For Economists. New Jersey, USA: WSPC, 2017.

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47

Kidd, Sarah E., and Catriona L. Halliday. Dematiaceous fungi. Edited by Christopher C. Kibbler, Richard Barton, Neil A. R. Gow, Susan Howell, Donna M. MacCallum, and Rohini J. Manuel. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780198755388.003.0014.

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The dematiaceous fungal pathogens, classified by their darkly pigmented hyphae, cause infection in both immunosuppressed and immunocompetent individuals. Infections may present as chromoblastomycosis, mycetoma, and a spectrum of phaeohyphomycoses varying in severity. The route of infection may be through traumatic inoculation, or inhalation with or without dissemination. A number of species are considered neurotropic and can cause cerebral abscesses in immunocompetent persons. Infections can occur worldwide, but are most common in the tropics, and some species appear to have specific geographic ranges. Diagnosis requires sampling at the site of infection; direct microscopy using KOH (potassium hydroxide), haematoxylin and eosin, and/or Fontana-Masson stains; and culturing. Accurate species identification is essential. Treatment includes antifungal therapy with or without surgery, and antifungal susceptibility testing is recommended for all cultures.

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48

Bird, Alexander. Knowing Science. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780199606658.001.0001.

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Abstract Knowing Science presents an epistemology of science that rejects empiricism and gives a central place to the concept of knowledge. Science aims at knowledge and progresses when it adds to the stock of knowledge. That knowledge is social knowing—it is known by the scientific community as a whole. Evidence is that from which knowledge can be obtained by inference. From which it follows that evidence is knowledge. Evidence is not limited to perception, nor to observation. Observation supplies evidence that is basic relative to a field of enquiry and can be highly non-perceptual. Theoretical knowledge is typically gained by inference to the only explanation, in which competing plausible hypotheses are falsified by the evidence. In cases where not all competing hypotheses are refuted, scientific hypotheses are not known but possess varying degrees of plausibility. Plausibilities in the light of the evidence are probabilities and link eliminative explanationism to Bayesian conditionalization. Scientific realism and anti-realism are considered as metascientific claims. Such global metascientific claims are rejected—track records give us only local metascientific claims.

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49

Carpentier, Chloé, Luis Royuela, Linda Montanari, and Philip Davis. The Global Epidemiology of Drug Use in Prison. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199374847.003.0002.

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This chapter provides an overview of drug use in prison. It is centered on illicit drug use in Europe, where the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) has been collecting aggregated data from various sources for 15 years. It also reviews, based on published literature, data from the four other global regions. A total of 59 studies from 31 countries in the five world regions were included for analysis. Results show that the prevalence of drug use varies greatly between studies. Lifetime prevalence of any illicit drug use in prison ranges between 2% to 76% worldwide with, in most cases, cannabis being the most frequently reported substance. More recent (past-month) illicit drug use is reported by <1% to 65% of inmates, while <1% to 39% report injecting illicit drugs during their stay in prison. Alcohol use in prison is reported in a few studies only, varying between 2% to 77% while the prevalence of current tobacco smoking ranges between 4% to 90%. In general, available data are scarce and patchy, especially outside Europe, with large variations in methodology relating to sampling strategy and measurement. This heterogeneity hampers comparison and may, in part, account for the wide range of prevalence estimates. Comparable methods and measures of drug use and its consequences in prison populations are needed to facilitate international comparisons and provide the sound information needed for development and implementation of drug interventions in various prison settings across the globe.

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50

Taxman, Faye S., and Mary Mun. Recidivism. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780199374847.003.0013.

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High rates of rearrest and recidivism, especially among drug-involved individuals, are of grave concern for the justice system and society at large. This chapter looks at the factors affecting recidivism rates of substance-involved individuals involved in the justice system. We begin by considering the complexity of measuring recidivism and the meaning of this concept; the term is fraught with difficulties due to the complexities of generalizing findings across studies with varying sampling frames and time-frames for follow-up, and differences in the types of recidivism events studied. Recent research illustrates that recidivism rates among drug users vary by drug of choice and are typically higher among individuals who use amphetamines, heroin, and/or cocaine. Recidivism rates may also vary depending on the presence of certain comorbid factors, although this is an emerging area of research. Factors that appear to elevate recidivism rates include personality disorders, co-occurring substance abuse and mental health disorders, other psychiatric disorders, and other serious mental illness. The location of an individual’s residence also appears to impact the recidivism rate, possibly mediated by the presence or absence of various protective factors in the community. While the nature of the relationship between drugs and crime is still unclear, the same is true for our understanding of recidivism among substance users in the justice system. There is a need for a greater understanding of the relationship between substance use and recidivism, in order to fill existing knowledge gaps.

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