標(biāo)題: Titlebook: Advanced Linear Modeling; Statistical Learning Ronald Christensen Textbook 2019Latest edition Springer Nature Switzerland AG 2019 ANOVA.Exc [打印本頁] 作者: Arthur 時(shí)間: 2025-3-21 19:28
書目名稱Advanced Linear Modeling影響因子(影響力)
書目名稱Advanced Linear Modeling影響因子(影響力)學(xué)科排名
書目名稱Advanced Linear Modeling網(wǎng)絡(luò)公開度
書目名稱Advanced Linear Modeling網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Advanced Linear Modeling被引頻次
書目名稱Advanced Linear Modeling被引頻次學(xué)科排名
書目名稱Advanced Linear Modeling年度引用
書目名稱Advanced Linear Modeling年度引用學(xué)科排名
書目名稱Advanced Linear Modeling讀者反饋
書目名稱Advanced Linear Modeling讀者反饋學(xué)科排名
作者: 朦朧 時(shí)間: 2025-3-21 22:21 作者: obnoxious 時(shí)間: 2025-3-22 04:02
Frequency Analysis of Time Series,duction is helpful for policymakers, researchers, students, and other people to upgrade life quality. Such knowledge is valuable because it is up-to-date, generalizable, and sensible based on the analytic-functionalist theoretical framework and statistical estimation.作者: conception 時(shí)間: 2025-3-22 06:19 作者: Triglyceride 時(shí)間: 2025-3-22 08:53
Linear Models for Spatial Data: Kriging,e theory we develop in this chapter will provide the basis for the nonparametric models of technology developed in Chapter 4 and the efficiency and productivity analysis undertaken in Parts II and III.作者: conference 時(shí)間: 2025-3-22 15:40
1431-875X ers. are made throughout the volume, .Advanced Linear Modeling. can be used?on its own given a solid background in linear models.? Accompanying R code for the?analyses is available online..978-3-030-29166-2978-3-030-29164-8Series ISSN 1431-875X Series E-ISSN 2197-4136 作者: 抒情短詩 時(shí)間: 2025-3-22 18:45 作者: 形上升才刺激 時(shí)間: 2025-3-22 21:54
Generalized Multivariate Linear Models and Longitudinal Data,n of adaptive concepts, which are capable of placing computational resources automatically where needed to realize the desired quality of the solution, e.g. in terms of error tolerances, at the lowest computational effort.作者: 冷漠 時(shí)間: 2025-3-23 05:06
Mixed Models and Variance Components, greater. Improved knowledge of mechanisms, signaling pathways, and molecular interactions with the digestive tract and host organism is necessary to exploit the potential of the microbiome to stabilize animal health.作者: evanescent 時(shí)間: 2025-3-23 06:48 作者: Bravado 時(shí)間: 2025-3-23 11:29
Ronald ChristensenPresents a collection of methodologies formulated and developed in the framework of linear models.Offers accompanying R code online for the included analyses.Features several new chapters, as well as 作者: Dri727 時(shí)間: 2025-3-23 15:26 作者: 作繭自縛 時(shí)間: 2025-3-23 18:23
https://doi.org/10.1007/978-3-030-29164-8ANOVA; Excel; Factor analysis; STATISTICA; Time series; data analysis; mathematical statistics; heterosceda作者: 古代 時(shí)間: 2025-3-23 23:56 作者: JAUNT 時(shí)間: 2025-3-24 04:26
Advanced Linear Modeling978-3-030-29164-8Series ISSN 1431-875X Series E-ISSN 2197-4136 作者: Barrister 時(shí)間: 2025-3-24 09:21
https://doi.org/10.1007/978-981-99-1051-9he data that the models lose their ability to make effective predictions. One way to stop overfitting is by using penalized estimation (regularization) methods. Penalized estimation provides an automated method of keeping the estimates from tracking the data more closely than is justified.作者: 保留 時(shí)間: 2025-3-24 13:09
Secure Web Gateway on Website in Cloudr heteroscedasticity is known. It then introduces general ideas for estimating dependence or heteroscedasticity when their exact natures are unknown. Most of the book, after this chapter, consists of applications of these ideas to specific models.作者: 郊外 時(shí)間: 2025-3-24 18:13
https://doi.org/10.1007/978-981-99-1051-9he data that the models lose their ability to make effective predictions. One way to stop overfitting is by using penalized estimation (regularization) methods. Penalized estimation provides an automated method of keeping the estimates from tracking the data more closely than is justified.作者: OMIT 時(shí)間: 2025-3-24 22:11
Secure Web Gateway on Website in Cloudr heteroscedasticity is known. It then introduces general ideas for estimating dependence or heteroscedasticity when their exact natures are unknown. Most of the book, after this chapter, consists of applications of these ideas to specific models.作者: erythema 時(shí)間: 2025-3-25 00:21 作者: V切開 時(shí)間: 2025-3-25 05:46
Studies in Computational Intelligences on each individual sampled, and then examining how those variables relate to one another. Discrimination problems have a very different sampling scheme. In discrimination problems data are obtained from multiple groups and we seek efficient means of telling the groups apart, i.e., discriminating b作者: 溫室 時(shí)間: 2025-3-25 09:46 作者: mastoid-bone 時(shí)間: 2025-3-25 13:27
https://doi.org/10.1007/978-3-030-87304-2This chapter introduces nonparametric regression for a single predictor variable, discusses the curse of dimensionality that plagues nonparametric regression with multiple predictor variables, and discusses the kernel trick and related ideas as methods for overcoming the curse of dimensionality.作者: monologue 時(shí)間: 2025-3-25 18:55
Human Odor Security Using E-noseThis chapter introduces an elegant mathematical theory that has been developed for nonparametric regression with penalized estimation.作者: 燈絲 時(shí)間: 2025-3-25 22:20
Human Odor Security Using E-noseThis chapter particularizes the results of Chap. . for linear mixed models with special emphasis on variance component models and a particular longitudinal data model.作者: Dorsal-Kyphosis 時(shí)間: 2025-3-26 03:33
https://doi.org/10.1007/978-981-99-1051-9This chapter examines the linear mixed models from Chap. . that have traditionally been used to analyze time series data. It also examines spectral distributions/densities and linear filtering of time series.作者: phytochemicals 時(shí)間: 2025-3-26 07:48
https://doi.org/10.1007/978-3-031-53385-3This chapter develops Box-Jenkins models. These involve applying the linear filters of Chap. . to white noise. It also introduces state-space models and the Kalman filter.作者: AVERT 時(shí)間: 2025-3-26 09:15
Big Data and Data Science EngineeringThis chapter addresses linear models for spatial data. A key aspect is the introduction of models for the covariance between data points separated in space. The same ideas can be used to model time series but, unlike the methods in the previous two chapters, time is not required to be observed at regular intervals.作者: 形狀 時(shí)間: 2025-3-26 12:52
Big Data and Data Science EngineeringThis chapter introduces the basic theory for linear models with more than one dependent variable.作者: OUTRE 時(shí)間: 2025-3-26 17:28 作者: 薄膜 時(shí)間: 2025-3-26 23:09
Hong-Linh Truong,Schahram DustdarThis chapter examines support vector machines. To do that properly it includes background on binomial regression and discrimination. Much of the technical material on support vector machines is relegated to Appendix A.作者: granite 時(shí)間: 2025-3-27 01:42
Penalized Estimation,ent a policy which can encourage producers to improve the environmental performance of their products thus stimulating consumers to give up their habit of making purchases. These labels/declarations encourage the supply and the demand of those products and services able to cause lower damage to the 作者: Myosin 時(shí)間: 2025-3-27 07:36
Reproducing Kernel Hilbert Spaces,wkins, it is a liminal space between the town, the forest and the Upside Down. Throughout the course of the first season, its small, simple, neutral-coloured interior transforms as Joyce becomes increasingly desperate to communicate with her son and bring him home from another dimension. Although th作者: Occlusion 時(shí)間: 2025-3-27 13:17 作者: Goblet-Cells 時(shí)間: 2025-3-27 14:33 作者: HERTZ 時(shí)間: 2025-3-27 20:56
Time Domain Analysis, not pay for the firm to buy more, because the purchase price is higher and the sales price is lower than the internal value of the production factor to the firm. The larger the difference is between purchase and sales price, the higher the probability that a production factor will become a fixed fa作者: Anticoagulant 時(shí)間: 2025-3-28 01:33
Linear Models for Spatial Data: Kriging, some period of time). Firms, however, produce different outputs, and sometimes it is difficult to find an appropriate aggregation of output to serve as an index, especially in the services industries. For this and other reasons, a more general theory of technology is required. There are two compone作者: 浪費(fèi)物質(zhì) 時(shí)間: 2025-3-28 02:17 作者: acetylcholine 時(shí)間: 2025-3-28 07:16 作者: creditor 時(shí)間: 2025-3-28 13:29
1431-875X included analyses.Features several new chapters, as well as Now in its third edition, this companion volume to Ronald Christensen’s. Plane?.Answers to Complex Questions. uses three fundamental concepts from standard linear model theory—best linear prediction, projections, and Mahalanobis distance— t作者: antiquated 時(shí)間: 2025-3-28 18:03
Big Data and Data Science Engineeringcurve models, GMANOVA models). Finally, we consider testing for whether a subset of the dependent variables actually provides us with additional information over and above the variables not considered in the subset. In Chap. . testing for additional information is seen as an important tool in linear discriminant analysis.作者: HPA533 時(shí)間: 2025-3-28 21:54 作者: 異常 時(shí)間: 2025-3-28 23:14
Studies in Computational Intelligence analysis of variance addresses the question of whether the groups are different whereas discriminant analysis seeks to specify how the groups are different. Allocation is the problem of assigning new individuals to their appropriate group. Allocation procedures have immediate application to diagnosing medical conditions.作者: 記憶法 時(shí)間: 2025-3-29 04:37 作者: –吃 時(shí)間: 2025-3-29 09:12
Textbook 2019Latest editionndard linear model theory—best linear prediction, projections, and Mahalanobis distance— to extend?standard linear modeling into the realms of Statistical Learning and Dependent Data. ?.This new edition features a wealth of new and revised content.? In Statistical?Learning it delves into nonparametr作者: Intractable 時(shí)間: 2025-3-29 15:17
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