Statistical Theory and Inference

This text is for  a one semester graduate course in statistical theory and covers minimal and complete sufficient statistics, maximum likelihood estimators, method of moments, bias and mean square error, uniform minimum variance estimators and the Cramer-Rao lower bound, an introduction to large sam...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Olive, David J. (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2014.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
LEADER 02651nam a22004335i 4500
001 978-3-319-04972-4
003 DE-He213
005 20151204185022.0
007 cr nn 008mamaa
008 140507s2014 gw | s |||| 0|eng d
020 |a 9783319049724  |9 978-3-319-04972-4 
024 7 |a 10.1007/978-3-319-04972-4  |2 doi 
040 |d GrThAP 
050 4 |a QA276-280 
072 7 |a PBT  |2 bicssc 
072 7 |a MAT029000  |2 bisacsh 
082 0 4 |a 519.5  |2 23 
100 1 |a Olive, David J.  |e author. 
245 1 0 |a Statistical Theory and Inference  |h [electronic resource] /  |c by David J. Olive. 
264 1 |a Cham :  |b Springer International Publishing :  |b Imprint: Springer,  |c 2014. 
300 |a XII, 434 p. 8 illus.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
505 0 |a Probability and Expectations.- Multivariate Distributions -- Exponential Families.- Sufficient Statistics.- Point Estimation I.-Point Estimation II -- Testing Statistical Hypotheses.- Large Sample Theory.- Confidence Intervals.- Some Useful Distributions -- Bayesian Methods -- Stuff for Students. 
520 |a This text is for  a one semester graduate course in statistical theory and covers minimal and complete sufficient statistics, maximum likelihood estimators, method of moments, bias and mean square error, uniform minimum variance estimators and the Cramer-Rao lower bound, an introduction to large sample theory, likelihood ratio tests and uniformly most powerful  tests and the Neyman Pearson Lemma. A major goal of this text is to make these topics much more accessible to students by using the theory of exponential families. Exponential families, indicator functions and the support of the distribution are used throughout the text to simplify the theory. More than 50 ``brand name" distributions are used to illustrate the theory with many examples of exponential families, maximum likelihood estimators and uniformly minimum variance unbiased estimators. There are many homework problems with over 30 pages of solutions. 
650 0 |a Statistics. 
650 0 |a Probabilities. 
650 1 4 |a Statistics. 
650 2 4 |a Statistical Theory and Methods. 
650 2 4 |a Probability Theory and Stochastic Processes. 
650 2 4 |a Statistics, general. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783319049717 
856 4 0 |u http://dx.doi.org/10.1007/978-3-319-04972-4  |z Full Text via HEAL-Link 
912 |a ZDB-2-SMA 
950 |a Mathematics and Statistics (Springer-11649)