Asymptotic Statistical Inference: A Basic Course Using R Deshmukh Shailaja
Asymptotic Statistical Inference: A Basic Course Using R Deshmukh Shailaja The book presents the fundamental concepts from asymptotic statistical inference theory, elaborating on some basic large…
Specifikacia Asymptotic Statistical Inference: A Basic Course Using R Deshmukh Shailaja
Asymptotic Statistical Inference: A Basic Course Using R Deshmukh Shailaja
The book presents the fundamental concepts from asymptotic statistical inference theory, elaborating on some basic large sample optimality properties of estimators and some test procedures. It is shown that for the probability models belonging to an exponential family and a Cramer family, the maximum likelihood estimators of the indexing parameters are CAN. The most desirable property of consistency of an estimator and its large sample distribution, with suitable normalization, are discussed, the focus being on the consistent and asymptotically normal (CAN) estimators.
Various applications of the likelihood ratio test procedure are addressed, when the underlying probability model is a multinomial distribution. The book describes some large sample test procedures, in particular, the most frequently used likelihood ratio test procedure. These include tests for the goodness of fit