語系:
繁體中文
English
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
Fundamentals of high-dimensional sta...
~
Lederer, Johannes.
FindBook
Google Book
Amazon
博客來
Fundamentals of high-dimensional statistics = with exercises and R labs /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Fundamentals of high-dimensional statistics/ by Johannes Lederer.
其他題名:
with exercises and R labs /
作者:
Lederer, Johannes.
出版者:
Cham :Springer International Publishing : : 2022.,
面頁冊數:
xiv, 355 p. :ill., digital ;24 cm.
內容註:
Preface -- Notation -- Introduction -- Linear Regression -- Graphical Models -- Tuning-Parameter Calibration -- Inference -- Theory I: Prediction -- Theory II: Estimation and Support Recovery -- A Solutions -- B Mathematical Background -- Bibliography -- Index.
Contained By:
Springer Nature eBook
標題:
Mathematical statistics. -
電子資源:
https://doi.org/10.1007/978-3-030-73792-4
ISBN:
9783030737924
Fundamentals of high-dimensional statistics = with exercises and R labs /
Lederer, Johannes.
Fundamentals of high-dimensional statistics
with exercises and R labs /[electronic resource] :by Johannes Lederer. - Cham :Springer International Publishing :2022. - xiv, 355 p. :ill., digital ;24 cm. - Springer texts in statistics,2197-4136. - Springer texts in statistics..
Preface -- Notation -- Introduction -- Linear Regression -- Graphical Models -- Tuning-Parameter Calibration -- Inference -- Theory I: Prediction -- Theory II: Estimation and Support Recovery -- A Solutions -- B Mathematical Background -- Bibliography -- Index.
This textbook provides a step-by-step introduction to the tools and principles of high-dimensional statistics. Each chapter is complemented by numerous exercises, many of them with detailed solutions, and computer labs in R that convey valuable practical insights. The book covers the theory and practice of high-dimensional linear regression, graphical models, and inference, ensuring readers have a smooth start in the field. It also offers suggestions for further reading. Given its scope, the textbook is intended for beginning graduate and advanced undergraduate students in statistics, biostatistics, and bioinformatics, though it will be equally useful to a broader audience.
ISBN: 9783030737924
Standard No.: 10.1007/978-3-030-73792-4doiSubjects--Topical Terms:
516858
Mathematical statistics.
LC Class. No.: QA276
Dewey Class. No.: 519.5
Fundamentals of high-dimensional statistics = with exercises and R labs /
LDR
:01987nmm a2200337 a 4500
001
2296380
003
DE-He213
005
20211116152307.0
006
m d
007
cr nn 008maaau
008
230324s2022 sz s 0 eng d
020
$a
9783030737924
$q
(electronic bk.)
020
$a
9783030737917
$q
(paper)
024
7
$a
10.1007/978-3-030-73792-4
$2
doi
035
$a
978-3-030-73792-4
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA276
072
7
$a
PBT
$2
bicssc
072
7
$a
MAT029000
$2
bisacsh
072
7
$a
PBT
$2
thema
082
0 4
$a
519.5
$2
23
090
$a
QA276
$b
.L473 2022
100
1
$a
Lederer, Johannes.
$3
3591022
245
1 0
$a
Fundamentals of high-dimensional statistics
$h
[electronic resource] :
$b
with exercises and R labs /
$c
by Johannes Lederer.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2022.
300
$a
xiv, 355 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
Springer texts in statistics,
$x
2197-4136
505
0
$a
Preface -- Notation -- Introduction -- Linear Regression -- Graphical Models -- Tuning-Parameter Calibration -- Inference -- Theory I: Prediction -- Theory II: Estimation and Support Recovery -- A Solutions -- B Mathematical Background -- Bibliography -- Index.
520
$a
This textbook provides a step-by-step introduction to the tools and principles of high-dimensional statistics. Each chapter is complemented by numerous exercises, many of them with detailed solutions, and computer labs in R that convey valuable practical insights. The book covers the theory and practice of high-dimensional linear regression, graphical models, and inference, ensuring readers have a smooth start in the field. It also offers suggestions for further reading. Given its scope, the textbook is intended for beginning graduate and advanced undergraduate students in statistics, biostatistics, and bioinformatics, though it will be equally useful to a broader audience.
650
0
$a
Mathematical statistics.
$3
516858
650
1 4
$a
Statistical Theory and Methods.
$3
891074
650
2 4
$a
Big Data.
$3
3134868
650
2 4
$a
Data Structures and Information Theory.
$3
3382368
650
2 4
$a
Artificial Intelligence.
$3
769149
650
2 4
$a
Statistics and Computing/Statistics Programs.
$3
894293
650
2 4
$a
Machine Learning.
$3
3382522
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer Nature eBook
830
0
$a
Springer texts in statistics.
$3
1567152
856
4 0
$u
https://doi.org/10.1007/978-3-030-73792-4
950
$a
Mathematics and Statistics (SpringerNature-11649)
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9438283
電子資源
11.線上閱覽_V
電子書
EB QA276
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入