標(biāo)題: Titlebook: Bayesian and Frequentist Regression Methods; Jon Wakefield Textbook 2013 Springer Science+Business Media, LLC, part of Springer Nature 201 [打印本頁] 作者: ARRAY 時間: 2025-3-21 16:29
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書目名稱Bayesian and Frequentist Regression Methods讀者反饋學(xué)科排名
作者: Fecundity 時間: 2025-3-21 21:08 作者: 鈍劍 時間: 2025-3-22 01:57 作者: 尋找 時間: 2025-3-22 06:25
https://doi.org/10.1007/978-3-540-73345-4We begin with two properties of determinants:作者: Gyrate 時間: 2025-3-22 11:33 作者: ANTH 時間: 2025-3-22 15:06
Nonparametric Regression with Multiple PredictorsIn this chapter we describe how the methods described in Chaps. 10 and 11 may be extended to the situation in which there are multiple predictors. We also provide a description of methods for classification, concentrating on approaches that are more model, as opposed to algorithm based.作者: 推遲 時間: 2025-3-22 21:03
Differentiation of Matrix ExpressionsFor univariate . and . :.→. we write the derivative as作者: tic-douloureux 時間: 2025-3-22 21:20 作者: Enzyme 時間: 2025-3-23 01:50
Jon WakefieldProvides a balanced, modern summary of Bayesian and frequentist methods for regression analysis.A book website contains R code to reproduce all of the analyses and figures in the book: http://faculty.作者: commensurate 時間: 2025-3-23 09:33 作者: Cholesterol 時間: 2025-3-23 11:40 作者: 正式演說 時間: 2025-3-23 17:35 作者: 厚顏 時間: 2025-3-23 22:03
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.作者: 并入 時間: 2025-3-23 23:55
https://doi.org/10.1007/978-1-4419-0925-1Bayes; Frequentist Methods; Inference; Modeling; Regression Analysis作者: 女歌星 時間: 2025-3-24 05:28
978-1-4939-3862-9Springer Science+Business Media, LLC, part of Springer Nature 2013作者: BLAZE 時間: 2025-3-24 08:49
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作者: progestin 時間: 2025-3-24 14:23 作者: 潔凈 時間: 2025-3-24 17:05
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作者: Accrue 時間: 2025-3-24 19:00
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-作者: 永久 時間: 2025-3-25 00:33
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作者: 收集 時間: 2025-3-25 03:54
Daiki Muroya,Kazuhisa Seta,Yuki Hayashiy contexts, with three common situations being when sampling is over time, space, or within families. We do not discuss pure time series applications in which data are collected over a single (usually long) series; this is a vast topic with many specialized texts. Generically, we consider regression作者: 真繁榮 時間: 2025-3-25 10:23 作者: 恭維 時間: 2025-3-25 12:43
Hypothesis Testing and Variable Selection tests is driven by the data. Section 4.9 provides a discussion of the impact on inference that the careless use of variable selection can have. Section 4.10 describes a pragmatic approach to variable selection. Concluding remarks appear in Section 4.11.作者: 心胸狹窄 時間: 2025-3-25 19:26
General Regression Models9.6 devoted to a Bayesian treatment. Section9.7 illustrates some of the flexibility of GLMMs by describing and applying a particular model for spatial dependence. An alternative random effects specification, based on conjugacy, is described in Sect.9.8. An important approach to the modeling and anal作者: Chauvinistic 時間: 2025-3-25 23:30 作者: 幻想 時間: 2025-3-26 01:03
Katsuya Hashimoto,Yoshio Nakatani tests is driven by the data. Section 4.9 provides a discussion of the impact on inference that the careless use of variable selection can have. Section 4.10 describes a pragmatic approach to variable selection. Concluding remarks appear in Section 4.11.作者: 無關(guān)緊要 時間: 2025-3-26 07:03 作者: 戲法 時間: 2025-3-26 08:40
0172-7397 all of the analyses and figures in the book: http://faculty..Bayesian and Frequentist Regression Methods.?provides a modern account of both Bayesian and frequentist methods of regression analysis. Many texts cover one or the other of the approaches, but this is the most comprehensive combination of 作者: GULF 時間: 2025-3-26 16:19
Konoki Tei,Toru Kano,Takako Akakura(nonrandom) quantities, and consequently there is no possibility of making probability statements about these unknowns. As the name suggests, the frequentist approach is characterized by a frequency view of probability, and the behavior of inferential procedures is evaluated under hypothetical repeated sampling of the data.作者: Diluge 時間: 2025-3-26 20:51 作者: 壯觀的游行 時間: 2025-3-26 22:09 作者: 抗生素 時間: 2025-3-27 02:12 作者: 孤僻 時間: 2025-3-27 07:25
Textbook 2013ementary components of the discussion. In particular, methods are illustrated using a variety of data sets. The majority of the data sets are drawn from biostatistics but the techniques are generalizable to a wide range of other disciplines..作者: NICE 時間: 2025-3-27 09:32
Textbook 2013ver one or the other of the approaches, but this is the most comprehensive combination of Bayesian and frequentist methods that exists in one place..The two philosophical approaches to regression methodology are featured here as complementary techniques, with theory and data analysis providing suppl作者: Nebulous 時間: 2025-3-27 16:59 作者: antidepressant 時間: 2025-3-27 18:12
Linear Modelsvailable over time for a group of units, we have . (also known as .) data, and each unit forms a cluster. We will often refer to the units as individuals. The methods described in PartII for calculating uncertainty measures (such as standard errors) are not applicable in situations in which the data are dependent.作者: BALK 時間: 2025-3-27 23:02
Konoki Tei,Toru Kano,Takako Akakurarall message of this book which is that in many instances, carefully thought out Bayesian and frequentist analyses will provide similar conclusions; however, situations in which one or the other approach may be preferred are also described.作者: Lignans 時間: 2025-3-28 04:27
Introduction and Motivating Examples,rall message of this book which is that in many instances, carefully thought out Bayesian and frequentist analyses will provide similar conclusions; however, situations in which one or the other approach may be preferred are also described.作者: Heterodoxy 時間: 2025-3-28 08:24 作者: alleviate 時間: 2025-3-28 10:37
Frequentist Inferencenges for unknown parameters that are supported by the data. Under the frequentist approach, parameters and hypotheses are viewed as unknown but fixed (nonrandom) quantities, and consequently there is no possibility of making probability statements about these unknowns. As the name suggests, the freq作者: majestic 時間: 2025-3-28 15:13
Bayesian Inferences 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作者: Blatant 時間: 2025-3-28 21:39 作者: 散布 時間: 2025-3-29 02:56
Linear Modelsl 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作者: 肉體 時間: 2025-3-29 06:54 作者: Thrombolysis 時間: 2025-3-29 11:10
General Regression Modelsed linear models (GLMs) and, more briefly, nonlinear models. We first give an outline of this chapter. In Sect.9.2 we describe three motivating datasets to which we return throughout the chapter. The GLMs discussed in Sect.6.3 can be extended to incorporate dependences in observations on the same un作者: 結(jié)合 時間: 2025-3-29 14:53
Book 1977wal. He was extremely active in the scientific world of alcoholism as planner and coordinator of three international conferences, as chairman of the section on Biomedical Research of the I.C.A.A., and as a member of a W.H.O. Task Force on Alcoholism. In addition, he was very active in a variety of s作者: painkillers 時間: 2025-3-29 16:56 作者: choleretic 時間: 2025-3-29 20:00 作者: 擔(dān)憂 時間: 2025-3-30 03:03
