Computer Age Statistical Inference: Algorithms, Evidence, and Data ScienceBradleyEfron and TrevorHastie Institute of Mathematical Statistics Monographs Cambridge University Press, 2016, (8th printing 2018), xix + 475 pages, $74.99, hardcover ISBN: 978‐1‐107‐14989‐2

2019 ◽  
Vol 87 (1) ◽  
pp. 186-188
Author(s):  
Reijo Sund
Keyword(s):  
Statistical Inference ◽  
Mathematical Statistics ◽  
Cambridge University ◽  
Inference Algorithms

scholarly journals Book review of: Computer Age Statistical Inference: Algorithms, Evidence, and Data Science by Bradley Efron and Trevor Hastie

2018 ◽  
Vol 24 (1) ◽  
pp. 77
Author(s):  
Marina Dobrota
Keyword(s):  
Statistical Inference ◽  
Data Science ◽  
Mathematical Statistics ◽  
Cambridge University ◽  
Inference Algorithms

Book review of: Computer Age Statistical Inference: Algorithms, Evidence, and Data Science by Bradley Efron and Trevor Hastie, Cambridge University Press, 2016, Series: Institute of Mathematical Statistics Monographs (5), 495pp., ISBN13: 9781107149892, ISBN10: 1107149894, Online ISBN: 9781316576533, DOI:10.1017/CBO9781316576533

scholarly journals Different kinds of sufficiency in the general Gauss-Markov model

2007 ◽  
Vol 57 (4) ◽  
Author(s):  
Andrzej Kornacki
Keyword(s):  
Markov Model ◽  
Statistical Inference ◽  
Mathematical Statistics ◽  
General Linear ◽  
Linear Sufficiency ◽  
Mutual Relations ◽  
Quadratic Sufficiency

AbstractSufficiency is one of the fundamental notions in mathematical statistics. In connection with the general linear Gauss-Markov model GM (y,Xβ, σ 2 V), there are some modifications of this notion such as linear sufficiency (Baksalary and Kala, Drygas) invariant linearly sufficiency (Oktaba, Kornacki, Wawrzosek) and quadratic sufficiency (Mueller). All these variants denote such transformations of the model GM that preserve properties essential in statistical inference. In the present paper we give mutual relations between above three classes of statistics.

Sign in / Sign up

Export Citation Format

Share Document