TY  - GEN
N2  - This book covers modern statistical inference based on likelihood with applications in medicine, epidemiology and biology. Two introductory chapters discuss the importance of statistical models in applied quantitative research and the central role of the likelihood function. The rest of the book is divided into three parts. The first describes likelihood-based inference from a frequentist viewpoint. Properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic are discussed in detail. In the second part, likelihood is combined with prior information to perform Bayesian inference. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods. Modern numerical techniques for Bayesian inference are described in a separate chapter. Finally two more advanced topics, model choice and prediction, are discussed both from a frequentist and a Bayesian perspective. A comprehensive appendix covers the necessary prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis.
DO  - 10.1007/978-3-642-37887-4
DO  - doi
AB  - This book covers modern statistical inference based on likelihood with applications in medicine, epidemiology and biology. Two introductory chapters discuss the importance of statistical models in applied quantitative research and the central role of the likelihood function. The rest of the book is divided into three parts. The first describes likelihood-based inference from a frequentist viewpoint. Properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic are discussed in detail. In the second part, likelihood is combined with prior information to perform Bayesian inference. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods. Modern numerical techniques for Bayesian inference are described in a separate chapter. Finally two more advanced topics, model choice and prediction, are discussed both from a frequentist and a Bayesian perspective. A comprehensive appendix covers the necessary prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis.
T1  - Applied statistical inferencelikelihood and Bayes /
AU  - Held, Leonhard,
AU  - Sabanés Bové, Daniel,
AU  - Held, Leonhard.
CN  - SpringerLink
CN  - QA276
ID  - 696185
KW  - Mathematical statistics.
KW  - Bayesian statistical decision theory.
KW  - Estimation theory.
SN  - 9783642378874 
SN  - 3642378870 
TI  - Applied statistical inferencelikelihood and Bayes /
LK  - https://univsouthin.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-3-642-37887-4
UR  - https://univsouthin.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-3-642-37887-4
ER  -