Course in Large Sample Theory
Regular price
€192.20
A01=Thomas S. Ferguson
advanced statistical inference methods
asymptotic
Asymptotic Distribution
Asymptotic Efficiency
Asymptotic Joint Distribution
Asymptotic Normality
asymptotic statistics
Author_Thomas S. Ferguson
Bernstein Von Mises Theorem
Category=PBT
Cauchy Distribution
central limit theorem
Continuity Theorem
distribution
Distribution Function
Double Exponential Distribution
Edgeworth Expansion
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Finite Fourth Moment
Fisher Information
Fisher Information Matrix
Independent Bernoulli Trials
Large Sample Theory
Lebesgue Dominated Convergence Theorem
Maximum Likelihood
maximum likelihood estimation
MLE
Multinomial Experiments
multivariate analysis
Noncentrality Parameter
Nonnegative Random Variables
probability theory
Randomization Test
Slutsky's Theorem
Slutsky’s Theorem
statistical convergence
Thomas S. Ferguson
Uniform Strong Law
Product details
- ISBN 9780412043710
- Weight: 480g
- Dimensions: 152 x 229mm
- Publication Date: 01 Jul 1996
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Paperback
Secure
checkout
Fast Shipping
Easy returns
A Course in Large Sample Theory is presented in four parts. The first treats basic probabilistic notions, the second features the basic statistical tools for expanding the theory, the third contains special topics as applications of the general theory, and the fourth covers more standard statistical topics. Nearly all topics are covered in their multivariate setting.
The book is intended as a first year graduate course in large sample theory for statisticians. It has been used by graduate students in statistics, biostatistics, mathematics, and related fields. Throughout the book there are many examples and exercises with solutions. It is an ideal text for self study.
