東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Advanced linear modeling = statistic...

Christensen, Ronald.

FindBook

Google Book

Amazon

博客來

Advanced linear modeling = statistical learning and dependent data /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Advanced linear modeling/ by Ronald Christensen.
其他題名:	statistical learning and dependent data /
作者:	Christensen, Ronald.
出版者:	Cham :Springer International Publishing : : 2019.,
面頁冊數:	xxiii, 608 p. :ill., digital ;24 cm.
內容註:	1. Nonparametric Regression -- 2. Penalized Estimation -- 3. Reproducing Kernel Hilbert Spaces -- 4. Covariance Parameter Estimation -- 5. Mixed Models and Variance Components -- 6. Frequency Analysis of Time Series -- 7. Time Domain Analysis -- 8. Linear Models for Spacial Data: Kriging -- 9. Multivariate Linear Models: General. 10. Multivariate Linear Models: Applications -- 11. Generalized Multivariate Linear Models and Longitudinal Data -- 12. Discrimination and Allocation -- 13. Binary Discrimination and Regression -- 14. Principal Components, Classical Multidimensional Scaling, and Factor Analysis -- A Mathematical Background -- B Best Linear Predictors -- C Residual Maximum Likelihood -- Index -- Author Index.
Contained By:	Springer eBooks
標題:	Linear models (Statistics) -
電子資源:	https://doi.org/10.1007/978-3-030-29164-8
ISBN:	9783030291648

Advanced linear modeling = statistical learning and dependent data /
Christensen, Ronald.

Advanced linear modelingstatistical learning and dependent data /[electronic resource] :by Ronald Christensen. - 3rd ed. - Cham :Springer International Publishing :2019. - xxiii, 608 p. :ill., digital ;24 cm. - Springer texts in statistics,1431-875X. - Springer texts in statistics..

1. Nonparametric Regression -- 2. Penalized Estimation -- 3. Reproducing Kernel Hilbert Spaces -- 4. Covariance Parameter Estimation -- 5. Mixed Models and Variance Components -- 6. Frequency Analysis of Time Series -- 7. Time Domain Analysis -- 8. Linear Models for Spacial Data: Kriging -- 9. Multivariate Linear Models: General. 10. Multivariate Linear Models: Applications -- 11. Generalized Multivariate Linear Models and Longitudinal Data -- 12. Discrimination and Allocation -- 13. Binary Discrimination and Regression -- 14. Principal Components, Classical Multidimensional Scaling, and Factor Analysis -- A Mathematical Background -- B Best Linear Predictors -- C Residual Maximum Likelihood -- Index -- Author Index.

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- 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 nonparametric regression, penalized estimation (regularization), reproducing kernel Hilbert spaces, the kernel trick, and support vector machines. For Dependent Data it uses linear model theory to examine general linear models, linear mixed models, time series, spatial data, (generalized) multivariate linear models, discrimination, and dimension reduction. While numerous references to Plane Answers 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.

ISBN: 9783030291648

Standard No.: 10.1007/978-3-030-29164-8doiSubjects--Topical Terms:

533190
Linear models (Statistics)

LC Class. No.: QA279 / .C47 2019

Dewey Class. No.: 519.5

Advanced linear modeling = statistical learning and dependent data /
LDR:02835nmm a2200361 a 4500 001 2219307
003 DE-He213
005 20191222011106.0
006 m d
007 cr nn 008maaau
008 201126s2019 gw s 0 eng d
020 $a 9783030291648 $q (electronic bk.)
020 $a 9783030291631 $q (paper)
024 7 $a 10.1007/978-3-030-29164-8 $2 doi
035 $a 978-3-030-29164-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA279 $b .C47 2019
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a PBWL $2 thema
082 0 4 $a 519.5 $2 23
090 $a QA279 $b .C554 2019
100 1 $a Christensen, Ronald. $3 1069707
245 1 0 $a Advanced linear modeling $h [electronic resource] : $b statistical learning and dependent data / $c by Ronald Christensen.
250 $a 3rd ed.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xxiii, 608 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer texts in statistics, $x 1431-875X
505 0 $a 1. Nonparametric Regression -- 2. Penalized Estimation -- 3. Reproducing Kernel Hilbert Spaces -- 4. Covariance Parameter Estimation -- 5. Mixed Models and Variance Components -- 6. Frequency Analysis of Time Series -- 7. Time Domain Analysis -- 8. Linear Models for Spacial Data: Kriging -- 9. Multivariate Linear Models: General. 10. Multivariate Linear Models: Applications -- 11. Generalized Multivariate Linear Models and Longitudinal Data -- 12. Discrimination and Allocation -- 13. Binary Discrimination and Regression -- 14. Principal Components, Classical Multidimensional Scaling, and Factor Analysis -- A Mathematical Background -- B Best Linear Predictors -- C Residual Maximum Likelihood -- Index -- Author Index.
520 $a 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- 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 nonparametric regression, penalized estimation (regularization), reproducing kernel Hilbert spaces, the kernel trick, and support vector machines. For Dependent Data it uses linear model theory to examine general linear models, linear mixed models, time series, spatial data, (generalized) multivariate linear models, discrimination, and dimension reduction. While numerous references to Plane Answers 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.
650 0 $a Linear models (Statistics) $3 533190
650 1 4 $a Probability Theory and Stochastic Processes. $3 891080
650 2 4 $a Computational Mathematics and Numerical Analysis. $3 891040
650 2 4 $a Statistical Theory and Methods. $3 891074
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Springer texts in statistics. $3 1567152
856 4 0 $u https://doi.org/10.1007/978-3-030-29164-8
950 $a Mathematics and Statistics (Springer-11649)