---

＜電子ブック＞
Model Selection and Inference : A Practical Information-Theoretic Approach / by Kenneth P. Burnham, David R. Anderson

版	1st ed. 1998.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	1998
大きさ	XX, 355 p. 10 illus : online resource
著者標目	*Burnham, Kenneth P author Anderson, David R author SpringerLink (Online service)
件　名	LCSH:Statistics  FREE:Statistical Theory and Methods
一般注記	1 Introduction -- 2 Information Theory and Log-Likelihood Models: A Basis for Model Selection and Inference -- 3 Practical Use of the Information-Theoretic Approach -- 4 Model-Selection Uncertainty with Examples -- 5 Monte Carlo and Example-Based Insights -- 6 Statistical Theory -- 7 Summary -- References We wrote this book to introduce graduate students and research workers in var­ ious scientific disciplines to the use of information-theoretic approaches in the analysis of empirical data. In its fully developed form, the information-theoretic approach allows inference based on more than one model (including estimates of unconditional precision); in its initial form, it is useful in selecting a "best" model and ranking the remaining models. We believe that often the critical issue in data analysis is the selection of a good approximating model that best represents the inference supported by the data (an estimated "best approximating model"). In­ formation theory includes the well-known Kullback-Leibler "distance" between two models (actually, probability distributions), and this represents a fundamental quantity in science. In 1973, Hirotugu Akaike derived an estimator of the (relative) Kullback-Leibler distance based on Fisher's maximized log-likelihood. His mea­ sure, now called Akaike 's information criterion (AIC), provided a new paradigm for model selection in the analysis of empirical data. His approach, with a funda­ mental link to information theory, is relatively simple and easy to use in practice, but little taught in statistics classes and far less understood in the applied sciences than should be the case. We do not accept the notion that there is a simple, "true model" in the biological sciences HTTP:URL=https://doi.org/10.1007/978-1-4757-2917-7

 類似資料