Titlebook: Bayesian and Frequentist Regression Methods; Jon Wakefield Textbook 2013 Springer Science+Business Media, LLC, part of Springer Nature 201

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正式演說

厚顏

https://doi.org/10.1007/978-3-642-21793-7l convenience and the relative ease of parameter interpretation. We discuss a number of issues that require consideration in order to perform a successful linear regression analysis. These issues are relevant irrespective of the inferential paradigm adopted and so apply to both frequentist and Bayesian analyses.

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https://doi.org/10.1007/978-1-4419-0925-1Bayes; Frequentist Methods; Inference; Modeling; Regression Analysis

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978-1-4939-3862-9Springer Science+Business Media, LLC, part of Springer Nature 2013

BLAZE

Konoki Tei,Toru Kano,Takako Akakurarmation—this endeavor is known as .. In this first chapter, we will begin in Sect.?1.2 by making some general comments about model formulation. In Sect.?1.3, a number of examples will be described in order to motivate the material to follow in the remainder of this book. In Sect.?1.4, we examine, in

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https://doi.org/10.1007/978-3-540-73354-6s in contrast to the frequentist view described in Chap. 2 in which parameters are treated as fixed .. Specifically, with respect to the inferential targets of Sect. 2.1, the fixed but unknown parameters and hypotheses are viewed as random variables under the Bayesian approach. Additionally, the unk

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Katsuya Hashimoto,Yoshio Nakatanis discussion, with an emphasis on critiquing the various approaches and on hypothesis testing in a regression setting. We examine both single and multiple hypothesis testing situations; Sects. 4.2 and 4.3 consider the frequentist and Bayesian approaches, respectively. Section 4.4 describes the well-

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https://doi.org/10.1007/978-3-642-21793-7l convenience and the relative ease of parameter interpretation. We discuss a number of issues that require consideration in order to perform a successful linear regression analysis. These issues are relevant irrespective of the inferential paradigm adopted and so apply to both frequentist and Bayes