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In All Likelihood: Statistical Modelling and Inference Using Likelihood
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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“Although these make a valuable contribution, currently they provide only a lower bound for heritability.35”
From Association of Genetic and Environmental Factors With Autism in a 5-Country Cohort (2019) · cites this paper as [35]
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Generalized additive models for location, scale and shape.
Rigby, R. A., & Stasinopoulos, D. M. (2005) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/j.1467-9876.2005.00510.x · Cited by 3,228
Warming caused by cumulative carbon emissions towards the trillionth tonne
Allen, M. R., Frame, D. J., Huntingford, C., JONES, C. D., Lowe, J. A., Meinshausen, M. A., & Meinshausen, N. (2009) · Nature · doi:10.1038/nature08019 · Cited by 1,691
Bayesian statistics and modelling
van de Schoot, R., Depaoli, S. A., King, R., Kramer, B. M. R., Märtens, K., Tadesse, M. G., Vannucci, M., Gelman, A., Veen, D., Willemsen, J. E., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-020-00001-2 · Cited by 1,338
Metadata
- Authors
- Pawitan, Y.
- Affiliations
- Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm, Sweden
- Publisher
- Oxford University PressOxford
- Published
- 2001-06-21
- 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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