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Cited by 877Statistical Methods and Bayesian InferenceStatistical Methods and InferenceProbability and Statistical Research

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In All Likelihood: Statistical Modelling and Inference Using Likelihood

Pawitan, Yudi·2001
DOI 10.1093/oso/9780198507659.001.0001
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Abstract

Based on a course in the theory of statistics this text concentrates on what can be achieved using the likelihood/Fisherian method of taking account of uncertainty when studying a statistical problem. It takes the concept ot the likelihood as providing the best methods for unifying the demands of statistical modelling and the theory of inference. Every likelihood concept is illustrated by realistic examples, which are not compromised by computational problems. Examples range from a simile comparison of two accident rates, to complex studies that require generalised linear or semiparametric modelling. The emphasis is that the likelihood is not simply a device to produce an estimate, but an important tool for modelling. The book generally takes an informal approach, where most important results are established using heuristic arguments and motivated with realistic examples. With the currently available computing power, examples are not contrived to allow a closed analytical solution, and the book can concentrate on the statistical aspects of the data modelling. In addition to classical likelihood theory, the book covers many modern topics such as generalized linear models and mixed models, non parametric smoothing, robustness, the EM algorithm and empirical likelihood.

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Metadata

Authors
Pawitan, Y.
Affiliations
Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm, Sweden
Publisher
Oxford University PressOxford
Published
2001-06-21
DOI
10.1093/oso/9780198507659.001.0001
OpenAlex
W4388322985
Cited by
874
References
0
Citations by year
2026: 29 · 2025: 40 · 2024: 44 · 2023: 32 · 2022: 46 · 2021: 44 · 2020: 44 · 2019: 68 · 2018: 40 · 2017: 48 · 2016: 43 · 2015: 42 · 2014: 53 · 2013: 43 · 2012: 42
FWCI
2.01
Type
book
Language
en
Open access
closed
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
  • Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
Keywords
likelihood principle, statistical modeling, generalized linear models, mixed models, nonparametric smoothing, robustness, expectation-maximization algorithm, empirical likelihood, uncertainty

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