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Books on the topic 'Discrete multivariate model'

Author: Grafiati

Published: 30 November 2024

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1

E, Fienberg Stephen, and Holland Paul W, eds. Discrete multivariate analysis: Theory and practice. Cambridge, Mass: MIT Press, 1988.

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2

Mullahy, John. Marginal effects in multivariate probit and kindred discrete and count outcome models, with applications in health economics. Cambridge, MA: National Bureau of Economic Research, 2011.

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3

Zelterman, Daniel. Models for Discrete Data. Oxford University Press, 2006.

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4

Zelterman, Daniel. Models for Discrete Data. Ebsco Publishing, 2006.

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5

Holland, Paul W., Yvonne M. Bishop, and Stephen Fienberg. Discrete Multivariate Analysis. Springer, 2008.

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6

Dezin, Aleksei A. Multidimensional Analysis and Discrete Models. Taylor & Francis Group, 2018.

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Gelfand, Alan, and Sujit K. Sahu. Models for demography of plant populations. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.17.

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This article discusses the use of Bayesian analysis and methods to analyse the demography of plant populations, and more specifically to estimate the demographic rates of trees and how they respond to environmental variation. It examines data from individual (tree) measurements over an eighteen-year period, including diameter, crown area, maturation status, and survival, and from seed traps, which provide indirect information on fecundity. The multiple data sets are synthesized with a process model where each individual is represented by a multivariate state-space submodel for both continuous (fecundity potential, growth rate, mortality risk, maturation probability) and discrete states (maturation status). The results from plant population demography analysis demonstrate the utility of hierarchical modelling as a mechanism for the synthesis of complex information and interactions.

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Dezin, Aleksei A. Multidimensional Analysis and Discrete Models. Taylor & Francis Group, 2018.

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Dezin, Aleksei A. Multidimensional Analysis and Discrete Models. Taylor & Francis Group, 2018.

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Dezin, Aleksei A. Multidimensional Analysis and Discrete Models. Taylor & Francis Group, 2018.

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11

Multidimensional Analysis and Discrete Models. Taylor & Francis Group, 2017.

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12

Siddiqi, Junaid Sagheer. Mixture and latent class models for discrete multivariate data. 1991.

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13

Discrete Multivariate Analysis: Theory and Practice. Springer London, Limited, 2007.

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14

Models for Discrete Longitudinal Data. Springer London, Limited, 2006.

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Models for Discrete Longitudinal Data. Springer New York, 2010.

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16

Models for Discrete Longitudinal Data (Springer Series in Statistics). Springer, 2005.

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17

Elwood, Mark. Chance variation. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199682898.003.0008.

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This chapter explains chance variation and statistical tests, including discrete and continuous measures, the concept of significance, one and two sided test, exact tests, precision and confidence limits. It shows tests of differences in proportions and chi-square tests, the Mantel-Haenszel test, and calculation of confidence limits, for simple tables and for stratified data. It covers heterogeneity tests, multiplicative and additive models, ordered exposure variables and tests of trend. It explains statistical tests for matched studies and in multivariate models. Multiple testing, the Bonferroni correction, issues of hypothesis testing and hypothesis generation, and subgroup analyses are discussed. Stopping rules and repeated testing in trials is covered. It explains how to calculate study power and the necessary size of the study. The chapter describes time to event analysis, including survival curves, product-limit and actuarial or life-table methods, and the calculation of confidence limits, relative survival ratios, the log rank test with control for confounding, and multivariate analysis.

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