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
Statistical learning and modeling in...
~
Balzano, Simona.
FindBook
Google Book
Amazon
博客來
Statistical learning and modeling in data analysis = methods and applications /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Statistical learning and modeling in data analysis/ edited by Simona Balzano ... [et al.].
其他題名:
methods and applications /
其他作者:
Balzano, Simona.
出版者:
Cham :Springer International Publishing : : 2021.,
面頁冊數:
viii, 182 p. :ill. (some col.), digital ;24 cm.
內容註:
Chapter 1 - Interpreting Effects in Generalized Linear Modeling (Alan Agresti, Claudia Tarantola, and Roberta Varriale) -- Chapter 2 - ACE, AVAS and Robust Data Transformations: Performance of Investment Funds (Anthony C. Atkinson, Marco Riani, Aldo Corbellini, and Gianluca Morelli) -- Chapter 3 - Predictive Principal Component Analysis (Simona Balzano, Maja Bozic, Laura Marcis, and Renato Salvatore) -- Chapter 4 - Robust model-based learning to discover new wheat varieties and discriminate adulterated kernels in X-ray images (Andrea Cappozzo, Francesca Greselin, and Thomas Brendan Murphy) -- Chapter 5 - A dynamic model for ordinal time series: an application to consumers' perceptions of inflation (Marcella Corduas) -- Chapter 6 - Deep learning to jointly analyze images and clinical data for disease detection (Federica Crobu and Agostino Di Ciaccio) -- Chapter 7 -Studying Affiliation Networks through Cluster CA and Blockmodeling (Daniela D'Ambrosio, Marco Serino, and Giancarlo Ragozini) -- Chapter 8 - Sectioning Procedure on Geostatistical Indices Series of Pavement Road Profiles (Mauro D'Apuzzo, Rose-Line Spacagna, Azzurra Evangelisti, Daniela Santilli, and Vittorio Nicolosi) -- Chapter 9 - Directional supervised learning through depth functions: an application to ECG waves analysis (Houyem Demni) -- Chapter 10 - Penalized vs. contrained approaches for clusterwise linear regression modelling (Roberto Di Mari, Stefano Antonio Gattone, and Roberto Rocci) -- Chapter 11 - Effect measures for group comparisons in a two-component mixture model: a cyber risk analysis (Maria Iannario and Claudia Tarantola) -- Chapter 12 - A Cramer-von Mises test of uniformity on the hypersphere (Eduardo Garcia-Portugues, Paula Navarro-Esteban, and Juan Antonio Cuesta-Albertos) -- Chapter 13 - On mean and/or variance mixtures of normal distributions (Sharon X. Lee and Geoffrey J. McLachlan) -- Chapter 14 - Robust depth-based inference in elliptical models (Stanislav Nagy and Jiri Dvorak) -- Chapter 15 - Latent class analysis for the derivation of marketing decisions: An empirical study for BEV battery manufacturers (Friederike Paetz) -- Chapter 16 - Small Area Estimation Diagnostics: the Case of the Fay-Herriot Model (Maria Chiara Pagliarella) -- Chapter 17 - A comparison between methods to cluster mixed-type data: Gaussian mixtures versus Gower distance (Monia Ranalli and Roberto Rocci) -- Chapter 18 - Exploring the gender gap in Erasmus student mobility flows (Marialuisa Restaino, Ilaria Primerano, and Maria Prosperina Vitale)
Contained By:
Springer Nature eBook
標題:
Statistics - Congresses. -
電子資源:
https://doi.org/10.1007/978-3-030-69944-4
ISBN:
9783030699444
Statistical learning and modeling in data analysis = methods and applications /
Statistical learning and modeling in data analysis
methods and applications /[electronic resource] :edited by Simona Balzano ... [et al.]. - Cham :Springer International Publishing :2021. - viii, 182 p. :ill. (some col.), digital ;24 cm. - Studies in classification, data analysis, and knowledge organization,1431-8814. - Studies in classification, data analysis, and knowledge organization..
Chapter 1 - Interpreting Effects in Generalized Linear Modeling (Alan Agresti, Claudia Tarantola, and Roberta Varriale) -- Chapter 2 - ACE, AVAS and Robust Data Transformations: Performance of Investment Funds (Anthony C. Atkinson, Marco Riani, Aldo Corbellini, and Gianluca Morelli) -- Chapter 3 - Predictive Principal Component Analysis (Simona Balzano, Maja Bozic, Laura Marcis, and Renato Salvatore) -- Chapter 4 - Robust model-based learning to discover new wheat varieties and discriminate adulterated kernels in X-ray images (Andrea Cappozzo, Francesca Greselin, and Thomas Brendan Murphy) -- Chapter 5 - A dynamic model for ordinal time series: an application to consumers' perceptions of inflation (Marcella Corduas) -- Chapter 6 - Deep learning to jointly analyze images and clinical data for disease detection (Federica Crobu and Agostino Di Ciaccio) -- Chapter 7 -Studying Affiliation Networks through Cluster CA and Blockmodeling (Daniela D'Ambrosio, Marco Serino, and Giancarlo Ragozini) -- Chapter 8 - Sectioning Procedure on Geostatistical Indices Series of Pavement Road Profiles (Mauro D'Apuzzo, Rose-Line Spacagna, Azzurra Evangelisti, Daniela Santilli, and Vittorio Nicolosi) -- Chapter 9 - Directional supervised learning through depth functions: an application to ECG waves analysis (Houyem Demni) -- Chapter 10 - Penalized vs. contrained approaches for clusterwise linear regression modelling (Roberto Di Mari, Stefano Antonio Gattone, and Roberto Rocci) -- Chapter 11 - Effect measures for group comparisons in a two-component mixture model: a cyber risk analysis (Maria Iannario and Claudia Tarantola) -- Chapter 12 - A Cramer-von Mises test of uniformity on the hypersphere (Eduardo Garcia-Portugues, Paula Navarro-Esteban, and Juan Antonio Cuesta-Albertos) -- Chapter 13 - On mean and/or variance mixtures of normal distributions (Sharon X. Lee and Geoffrey J. McLachlan) -- Chapter 14 - Robust depth-based inference in elliptical models (Stanislav Nagy and Jiri Dvorak) -- Chapter 15 - Latent class analysis for the derivation of marketing decisions: An empirical study for BEV battery manufacturers (Friederike Paetz) -- Chapter 16 - Small Area Estimation Diagnostics: the Case of the Fay-Herriot Model (Maria Chiara Pagliarella) -- Chapter 17 - A comparison between methods to cluster mixed-type data: Gaussian mixtures versus Gower distance (Monia Ranalli and Roberto Rocci) -- Chapter 18 - Exploring the gender gap in Erasmus student mobility flows (Marialuisa Restaino, Ilaria Primerano, and Maria Prosperina Vitale)
The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series of engaging real-world applications. The book covers numerous research topics, ranging from statistical inference and modeling to clustering and factorial methods, from directional data analysis to time series analysis and small area estimation. The applications reflect new analyses in a variety of fields, including medicine, finance, engineering, marketing and cyber risk. The book gathers selected and peer-reviewed contributions presented at the 12th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2019), held in Cassino, Italy, on September 11-13, 2019. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results. This book, true to CLADAG's goals, is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification.
ISBN: 9783030699444
Standard No.: 10.1007/978-3-030-69944-4doiSubjects--Topical Terms:
690172
Statistics
--Congresses.
LC Class. No.: QA276.A1 / S73 2021
Dewey Class. No.: 519.5
Statistical learning and modeling in data analysis = methods and applications /
LDR
:04928nmm a2200337 a 4500
001
2244875
003
DE-He213
005
20210713043756.0
006
m d
007
cr nn 008maaau
008
211207s2021 sz s 0 eng d
020
$a
9783030699444
$q
(electronic bk.)
020
$a
9783030699437
$q
(paper)
024
7
$a
10.1007/978-3-030-69944-4
$2
doi
035
$a
978-3-030-69944-4
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA276.A1
$b
S73 2021
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.A1
$b
S797 2021
245
0 0
$a
Statistical learning and modeling in data analysis
$h
[electronic resource] :
$b
methods and applications /
$c
edited by Simona Balzano ... [et al.].
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2021.
300
$a
viii, 182 p. :
$b
ill. (some col.), digital ;
$c
24 cm.
490
1
$a
Studies in classification, data analysis, and knowledge organization,
$x
1431-8814
505
0
$a
Chapter 1 - Interpreting Effects in Generalized Linear Modeling (Alan Agresti, Claudia Tarantola, and Roberta Varriale) -- Chapter 2 - ACE, AVAS and Robust Data Transformations: Performance of Investment Funds (Anthony C. Atkinson, Marco Riani, Aldo Corbellini, and Gianluca Morelli) -- Chapter 3 - Predictive Principal Component Analysis (Simona Balzano, Maja Bozic, Laura Marcis, and Renato Salvatore) -- Chapter 4 - Robust model-based learning to discover new wheat varieties and discriminate adulterated kernels in X-ray images (Andrea Cappozzo, Francesca Greselin, and Thomas Brendan Murphy) -- Chapter 5 - A dynamic model for ordinal time series: an application to consumers' perceptions of inflation (Marcella Corduas) -- Chapter 6 - Deep learning to jointly analyze images and clinical data for disease detection (Federica Crobu and Agostino Di Ciaccio) -- Chapter 7 -Studying Affiliation Networks through Cluster CA and Blockmodeling (Daniela D'Ambrosio, Marco Serino, and Giancarlo Ragozini) -- Chapter 8 - Sectioning Procedure on Geostatistical Indices Series of Pavement Road Profiles (Mauro D'Apuzzo, Rose-Line Spacagna, Azzurra Evangelisti, Daniela Santilli, and Vittorio Nicolosi) -- Chapter 9 - Directional supervised learning through depth functions: an application to ECG waves analysis (Houyem Demni) -- Chapter 10 - Penalized vs. contrained approaches for clusterwise linear regression modelling (Roberto Di Mari, Stefano Antonio Gattone, and Roberto Rocci) -- Chapter 11 - Effect measures for group comparisons in a two-component mixture model: a cyber risk analysis (Maria Iannario and Claudia Tarantola) -- Chapter 12 - A Cramer-von Mises test of uniformity on the hypersphere (Eduardo Garcia-Portugues, Paula Navarro-Esteban, and Juan Antonio Cuesta-Albertos) -- Chapter 13 - On mean and/or variance mixtures of normal distributions (Sharon X. Lee and Geoffrey J. McLachlan) -- Chapter 14 - Robust depth-based inference in elliptical models (Stanislav Nagy and Jiri Dvorak) -- Chapter 15 - Latent class analysis for the derivation of marketing decisions: An empirical study for BEV battery manufacturers (Friederike Paetz) -- Chapter 16 - Small Area Estimation Diagnostics: the Case of the Fay-Herriot Model (Maria Chiara Pagliarella) -- Chapter 17 - A comparison between methods to cluster mixed-type data: Gaussian mixtures versus Gower distance (Monia Ranalli and Roberto Rocci) -- Chapter 18 - Exploring the gender gap in Erasmus student mobility flows (Marialuisa Restaino, Ilaria Primerano, and Maria Prosperina Vitale)
520
$a
The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series of engaging real-world applications. The book covers numerous research topics, ranging from statistical inference and modeling to clustering and factorial methods, from directional data analysis to time series analysis and small area estimation. The applications reflect new analyses in a variety of fields, including medicine, finance, engineering, marketing and cyber risk. The book gathers selected and peer-reviewed contributions presented at the 12th Scientific Meeting of the Classification and Data Analysis Group of the Italian Statistical Society (CLADAG 2019), held in Cassino, Italy, on September 11-13, 2019. CLADAG promotes advanced methodological research in multivariate statistics with a special focus on data analysis and classification, and supports the exchange and dissemination of ideas, methodological concepts, numerical methods, algorithms, and computational and applied results. This book, true to CLADAG's goals, is intended for researchers and practitioners who are interested in the latest developments and applications in the field of data analysis and classification.
650
0
$a
Statistics
$x
Congresses.
$3
690172
650
0
$a
Mathematical models
$x
Congresses.
$3
686297
650
1 4
$a
Statistical Theory and Methods.
$3
891074
650
2 4
$a
Statistics and Computing/Statistics Programs.
$3
894293
650
2 4
$a
Data Mining and Knowledge Discovery.
$3
898250
650
2 4
$a
Applied Statistics.
$3
3300946
650
2 4
$a
Machine Learning.
$3
3382522
650
2 4
$a
Data Structures and Information Theory.
$3
3382368
700
1
$a
Balzano, Simona.
$3
3506185
710
2
$a
SpringerLink (Online service)
$3
836513
773
0
$t
Springer Nature eBook
830
0
$a
Studies in classification, data analysis, and knowledge organization.
$3
1568262
856
4 0
$u
https://doi.org/10.1007/978-3-030-69944-4
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
W9405921
電子資源
11.線上閱覽_V
電子書
EB QA276.A1 S73 2021
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