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

Statistical causal inferences and th...

Wu, Pan.

FindBook

Google Book

Amazon

博客來

Statistical causal inferences and their applications in public health research

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical causal inferences and their applications in public health research/ edited by Hua He, Pan Wu, Ding-Geng (Din) Chen.
其他作者:	Wu, Pan.
出版者:	Cham :Springer International Publishing : : 2016.,
面頁冊數:	xv, 321 p. :ill., digital ;24 cm.
內容註:	Part I. Overview -- 1. Causal Inference - A Statistical Paradigm for Inferring Causality -- Part II. Propensity Score Method for Causal Inference -- 2. Overview of Propensity Score Methods -- 3. Sufficient Covariate, Propensity Variable and Doubly Robust Estimation -- 4. A Robustness Index of Propensity Score Estimation to Uncontrolled Confounders -- 5. Missing Confounder Data in Propensity Score Methods for Causal Inference -- 6. Propensity Score Modeling & Evaluation -- 7. Overcoming the Computing Barriers in Statistical Causal Inference -- Part III. Causal Inference in Randomized Clinical Studies -- 8. Semiparametric Theory and Empirical Processes in Causal Inference -- 9. Structural Nested Models for Cluster-Randomized Trials -- 10. Causal Models for Randomized Trials with Continuous Compliance -- 11. Causal Ensembles for Evaluating the Effect of Delayed Switch to Second-line Antiretroviral Regimens -- 12. Structural Functional Response Models for Complex Intervention Trials -- Part IV. Structural Equation Models for Mediation Analysis -- 13.Identification of Causal Mediation Models with An Unobserved Pre-treatment Confounder -- 14. A Comparison of Potential Outcome Approaches for Assessing Causal Mediation -- 15. Causal Mediation Analysis Using Structure Equation Models.
Contained By:	Springer eBooks
標題:	Mathematical statistics. -
電子資源:	http://dx.doi.org/10.1007/978-3-319-41259-7
ISBN:	9783319412597

Statistical causal inferences and their applications in public health research
Statistical causal inferences and their applications in public health research[electronic resource] /edited by Hua He, Pan Wu, Ding-Geng (Din) Chen. - Cham :Springer International Publishing :2016. - xv, 321 p. :ill., digital ;24 cm. - ICSA book series in statistics,2199-0980. - ICSA book series in statistics..

Part I. Overview -- 1. Causal Inference - A Statistical Paradigm for Inferring Causality -- Part II. Propensity Score Method for Causal Inference -- 2. Overview of Propensity Score Methods -- 3. Sufficient Covariate, Propensity Variable and Doubly Robust Estimation -- 4. A Robustness Index of Propensity Score Estimation to Uncontrolled Confounders -- 5. Missing Confounder Data in Propensity Score Methods for Causal Inference -- 6. Propensity Score Modeling & Evaluation -- 7. Overcoming the Computing Barriers in Statistical Causal Inference -- Part III. Causal Inference in Randomized Clinical Studies -- 8. Semiparametric Theory and Empirical Processes in Causal Inference -- 9. Structural Nested Models for Cluster-Randomized Trials -- 10. Causal Models for Randomized Trials with Continuous Compliance -- 11. Causal Ensembles for Evaluating the Effect of Delayed Switch to Second-line Antiretroviral Regimens -- 12. Structural Functional Response Models for Complex Intervention Trials -- Part IV. Structural Equation Models for Mediation Analysis -- 13.Identification of Causal Mediation Models with An Unobserved Pre-treatment Confounder -- 14. A Comparison of Potential Outcome Approaches for Assessing Causal Mediation -- 15. Causal Mediation Analysis Using Structure Equation Models.

This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate) It is particularly relevant for students pursuing degrees in Statistics, Biostatistics and Computational Biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.

ISBN: 9783319412597

Standard No.: 10.1007/978-3-319-41259-7doiSubjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Statistical causal inferences and their applications in public health research
LDR:03381nmm a2200337 a 4500 001 2053776
003 DE-He213
005 20161026162550.0
006 m d
007 cr nn 008maaau
008 170510s2016 gw s 0 eng d
020 $a 9783319412597 $q (electronic bk.)
020 $a 9783319412573 $q (paper)
024 7 $a 10.1007/978-3-319-41259-7 $2 doi
035 $a 978-3-319-41259-7
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276
072 7 $a PBT $2 bicssc
072 7 $a MBNS $2 bicssc
072 7 $a MED090000 $2 bisacsh
082 0 4 $a 519.5 $2 23
090 $a QA276 $b .S797 2016
245 0 0 $a Statistical causal inferences and their applications in public health research $h [electronic resource] / $c edited by Hua He, Pan Wu, Ding-Geng (Din) Chen.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xv, 321 p. : $b ill., digital ; $c 24 cm.
490 1 $a ICSA book series in statistics, $x 2199-0980
505 0 $a Part I. Overview -- 1. Causal Inference - A Statistical Paradigm for Inferring Causality -- Part II. Propensity Score Method for Causal Inference -- 2. Overview of Propensity Score Methods -- 3. Sufficient Covariate, Propensity Variable and Doubly Robust Estimation -- 4. A Robustness Index of Propensity Score Estimation to Uncontrolled Confounders -- 5. Missing Confounder Data in Propensity Score Methods for Causal Inference -- 6. Propensity Score Modeling & Evaluation -- 7. Overcoming the Computing Barriers in Statistical Causal Inference -- Part III. Causal Inference in Randomized Clinical Studies -- 8. Semiparametric Theory and Empirical Processes in Causal Inference -- 9. Structural Nested Models for Cluster-Randomized Trials -- 10. Causal Models for Randomized Trials with Continuous Compliance -- 11. Causal Ensembles for Evaluating the Effect of Delayed Switch to Second-line Antiretroviral Regimens -- 12. Structural Functional Response Models for Complex Intervention Trials -- Part IV. Structural Equation Models for Mediation Analysis -- 13.Identification of Causal Mediation Models with An Unobserved Pre-treatment Confounder -- 14. A Comparison of Potential Outcome Approaches for Assessing Causal Mediation -- 15. Causal Mediation Analysis Using Structure Equation Models.
520 $a This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate) It is particularly relevant for students pursuing degrees in Statistics, Biostatistics and Computational Biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.
650 0 $a Mathematical statistics. $3 516858
650 0 $a Statistics. $3 517247
650 0 $a Public health. $3 534748
650 0 $a Biometry. $3 531975
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 891086
650 2 4 $a Biostatistics. $3 1002712
650 2 4 $a Public Health. $3 624351
700 1 $a Wu, Pan. $3 3166894
700 1 $a Chen, Ding-Geng. $3 1097388
700 1 $a He, Hua. $3 2170797
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a ICSA book series in statistics. $3 2153476
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-41259-7
950 $a Mathematics and Statistics (Springer-11649)