標(biāo)題: Titlebook: Generalized Linear Models With Examples in R; Peter K. Dunn,Gordon K.‘Smyth Textbook 2018 Springer Science+Business Media, LLC, part of Sp [打印本頁] 作者: 積聚 時(shí)間: 2025-3-21 19:03
書目名稱Generalized Linear Models With Examples in R影響因子(影響力)
書目名稱Generalized Linear Models With Examples in R影響因子(影響力)學(xué)科排名
書目名稱Generalized Linear Models With Examples in R網(wǎng)絡(luò)公開度
書目名稱Generalized Linear Models With Examples in R網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Generalized Linear Models With Examples in R被引頻次
書目名稱Generalized Linear Models With Examples in R被引頻次學(xué)科排名
書目名稱Generalized Linear Models With Examples in R年度引用
書目名稱Generalized Linear Models With Examples in R年度引用學(xué)科排名
書目名稱Generalized Linear Models With Examples in R讀者反饋
書目名稱Generalized Linear Models With Examples in R讀者反饋學(xué)科排名
作者: nurture 時(shí)間: 2025-3-21 23:55
https://doi.org/10.1007/978-3-319-65043-2ameters are developed and matrix formulations are used to estimate the regression parameters. We then explore the important connection between the algorithms for fitting linear regression models and .s. Techniques are then developed for estimating . We conclude with a discussion of using .?to fit .s.作者: 貨物 時(shí)間: 2025-3-22 01:27
https://doi.org/10.1057/9781137313652t data described by covariates, has already been covered elsewhere. We then focus on describing models for rates and models for counts organized in tables. Overdispersion is then discussed, including a discussion negative binomial .s?and quasi-Poisson models as alternative models.作者: Petechiae 時(shí)間: 2025-3-22 07:19
Chapter 2: Linear Regression Models,sion coefficients, followed by analysis of variance methods. We then discuss methods for comparing nested models, and for comparing non-nested models. Tools to assist in model selection are then described.作者: 膽小鬼 時(shí)間: 2025-3-22 12:03 作者: 瑪瑙 時(shí)間: 2025-3-22 15:20 作者: 瑪瑙 時(shí)間: 2025-3-22 19:13
Textbook 2018 both beginning and advancedstudents of applied statistics. Generalized linear models (GLMs) arepowerful tools in applied statistics that extend the ideas of multiplelinear regression and analysis of variance to include response variablesthat are not normally distributed. As such, GLMs can model a w作者: Vulnerary 時(shí)間: 2025-3-22 22:23
What Is Constructing Test Items?,stematic component of the .?is then considered in greater detail (Sect.?.). Having discussed the two components of the ., .s?are then formally defined (Sect.?.), and the important concept of the deviance function is introduced (Sect.?.). Finally, using a .?is compared to using a regression model after transforming the response (Sect.?.).作者: Nonporous 時(shí)間: 2025-3-23 04:59
https://doi.org/10.1057/978-1-137-55854-1ersion asymptotic results (the large sample asymptotics do not apply), which are discussed in Sect.?. where guidelines are presented for when these results hold. We then consider inference when . is unknown (Sect.?.), and include a discussion of using the different estimates of ..作者: preeclampsia 時(shí)間: 2025-3-23 06:45 作者: Clinch 時(shí)間: 2025-3-23 12:07
Chapter 5: Generalized Linear Models: Structure,stematic component of the .?is then considered in greater detail (Sect.?.). Having discussed the two components of the ., .s?are then formally defined (Sect.?.), and the important concept of the deviance function is introduced (Sect.?.). Finally, using a .?is compared to using a regression model after transforming the response (Sect.?.).作者: 提名的名單 時(shí)間: 2025-3-23 17:28
Chapter 7: Generalized Linear Models: Inference,ersion asymptotic results (the large sample asymptotics do not apply), which are discussed in Sect.?. where guidelines are presented for when these results hold. We then consider inference when . is unknown (Sect.?.), and include a discussion of using the different estimates of ..作者: debase 時(shí)間: 2025-3-23 21:19
Chapter 8: Generalized Linear Models: Diagnostics,bout using each type of residual and the nomenclature of residuals are given in Sect.?.. We then discuss techniques to remedy or ameliorate any weaknesses in the models (Sect.?.), including the introduction of quasi-likelihood (Sect.?.). Finally, collinearity is discussed (Sect.?.).作者: 縮短 時(shí)間: 2025-3-24 01:13 作者: 顛簸地移動(dòng) 時(shí)間: 2025-3-24 04:33 作者: Pseudoephedrine 時(shí)間: 2025-3-24 10:11 作者: 模范 時(shí)間: 2025-3-24 14:34 作者: 冥界三河 時(shí)間: 2025-3-24 16:13
Chapter 11: Positive Continuous Data: Gamma and Inverse Gaussian GLMs,an be modelled. Modelling positive continuous data is introduced in Sect.?., then the two most common .s?for modelling positive continuous data are discussed: gamma distributions (Sect.?.) and inverse Gaussian distributions (Sect.?.). The use of link functions is then addressed (Sect.?.). Finally, estimation of . is considered in Sect.?..作者: 使迷醉 時(shí)間: 2025-3-24 21:17 作者: 不可比擬 時(shí)間: 2025-3-25 02:05
Youth and the Threats to Socialismregression models are first reviewed (Sect.?.), then residuals, the main tools of diagnostic analysis, are defined (Sect.?.). We follow with a discussion of the leverage, a measure of the location of an observation relative to the average observation location (Sect.?.). The various diagnostic tools 作者: Lipoma 時(shí)間: 2025-3-25 05:38 作者: chemoprevention 時(shí)間: 2025-3-25 10:24
Chapter 3: Linear Regression Models: Diagnostics and Model-Building,regression models are first reviewed (Sect.?.), then residuals, the main tools of diagnostic analysis, are defined (Sect.?.). We follow with a discussion of the leverage, a measure of the location of an observation relative to the average observation location (Sect.?.). The various diagnostic tools 作者: 1FAWN 時(shí)間: 2025-3-25 15:22 作者: Entirety 時(shí)間: 2025-3-25 19:33 作者: 捐助 時(shí)間: 2025-3-25 23:16 作者: blackout 時(shí)間: 2025-3-26 02:11
Chapter 2: Linear Regression Models,guage used in this book so a common foundation is laid for all readers for the upcoming study of generalized linear models: linear regression models are a special case of generalized linear models. We first define linear regression models and introduce the relevant notation and assumptions. We then 作者: 吞噬 時(shí)間: 2025-3-26 05:02 作者: Oration 時(shí)間: 2025-3-26 10:24 作者: 下邊深陷 時(shí)間: 2025-3-26 15:52 作者: 薄荷醇 時(shí)間: 2025-3-26 17:58
Chapter 7: Generalized Linear Models: Inference,re applied in the context of .s. We first consider inference when . is known (Sect.?.), then the large-sample asymptotic results (Sect.?.) that underlie all the distributional results for the test statistics in that section. Section?. then introduces goodness-of-fit tests to determine whether the li作者: 共同時(shí)代 時(shí)間: 2025-3-26 22:17 作者: 單純 時(shí)間: 2025-3-27 03:34 作者: 甜食 時(shí)間: 2025-3-27 06:21
Chapter 10: Models for Counts: Poisson and Negative Binomial GLMs, from a source of radiation in a given time; the number of cases of leukemia reported per year in a certain jurisdiction; the number of flaws per metre of electrical cable. This chapter is concerned with counts when the individual events being counted are independent, or nearly so, and where there i作者: PATHY 時(shí)間: 2025-3-27 12:21 作者: 旅行路線 時(shí)間: 2025-3-27 15:33 作者: heterodox 時(shí)間: 2025-3-27 19:41
Generalized Linear Models With Examples in R978-1-4419-0118-7Series ISSN 1431-875X Series E-ISSN 2197-4136 作者: obstinate 時(shí)間: 2025-3-28 01:11 作者: Hiatus 時(shí)間: 2025-3-28 04:38 作者: PACT 時(shí)間: 2025-3-28 08:01 作者: MURKY 時(shí)間: 2025-3-28 12:23
What Is Constructing Test Items?, Chap.?.. Generalized linear models (.s) assume the responses come from a distribution that belongs to a more general . of distributions, and also permit more general systematic components. We first review the two components of a .?(Sect.?.) then discuss in greater detail the family of distributions作者: Dna262 時(shí)間: 2025-3-28 16:44
https://doi.org/10.1007/978-3-319-65043-2ssion parameters and possibly the dispersion parameter .. Because .s?assume a specific probability distribution for the responses from the .?family, maximum likelihood estimation procedures are used for parameter estimation, and general formulae are developed for the .?context. We first derive the s作者: patriot 時(shí)間: 2025-3-28 21:50
https://doi.org/10.1057/978-1-137-55854-1re applied in the context of .s. We first consider inference when . is known (Sect.?.), then the large-sample asymptotic results (Sect.?.) that underlie all the distributional results for the test statistics in that section. Section?. then introduces goodness-of-fit tests to determine whether the li作者: 顯微鏡 時(shí)間: 2025-3-28 23:40
,“Living in the Twilight Zone”,umptions of the .?are first reviewed (Sect.?.), then the three basic types of residuals (Pearson, deviance and quantile) are defined (Sect.?.). The leverages are then given in the .?context (Sect.?.) leading to the development of standardized residuals (Sect.?.). The various diagnostic tools for che作者: 抒情短詩 時(shí)間: 2025-3-29 03:07
Menus with Advanced Scripting and DHTML,all .s. It is used to model proportions, where the proportions are obtained as the number of ‘positive’ cases out of a total number of independent cases. We first compile important information about the binomial distribution (Sect.?.), then discuss the common link functions used for binomial .s?(Sec作者: 季雨 時(shí)間: 2025-3-29 09:24 作者: Enervate 時(shí)間: 2025-3-29 13:40
https://doi.org/10.1007/978-3-662-55612-2cal quantity that is always present. The two most common .s?for this type of data are based on the gamma and inverse Gaussian distributions. Judicious choice of link function and transformations of the covariates ensure that a variety of relationships between the response and explanatory variables c
