This HTML5 document contains 26 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

PrefixNamespace IRI
marcrelhttp://id.loc.gov/vocabulary/relators/
dctermshttp://purl.org/dc/terms/
n7http://www.idref.fr/027576973/
n16http://www.idref.fr/118151339/
n17http://www.idref.fr/027830632/
dchttp://purl.org/dc/elements/1.1/
rdauhttp://rdaregistry.info/Elements/u/
skoshttp://www.w3.org/2004/02/skos/core#
n18http://lexvo.org/id/iso639-3/
n11http://iflastandards.info/ns/isbd/terms/contentform/
rdachttp://rdaregistry.info/Elements/c/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n12http://www.idref.fr/027296539/
frbrhttp://purl.org/vocab/frbr/core#
n10http://iflastandards.info/ns/isbd/elements/
n5http://rdaregistry.info/termList/RDAContentType/
rdawhttp://rdaregistry.info/Elements/w/
xsdhhttp://www.w3.org/2001/XMLSchema#
n2http://www.idref.fr/216378397/
Subject Item
n2:id
rdf:type
frbr:Work rdac:C10001
marcrel:aut
n16:id
dc:subject
Mathematical statistics Statistique mathématique Statistics  Law of large numbers Probability Theory and Stochastic Processes Statistics Statistical Theory and Methods Probability Theory Sampling (Statistics) Échantillonnage (statistique) Loi des grands nombres Probabilities
skos:prefLabel
Large Sample Techniques for Statistics
dcterms:language
n18:eng
dcterms:subject
n7:id n12:id n17:id
dc:title
Large Sample Techniques for Statistics
skos:note
This book offers a comprehensive guide to large sample techniques in statistics. With a focus on developing analytical skills and understanding motivation, Large Sample Techniques for Statistics begins with fundamental techniques, and connects theory and applications in engaging ways. The first five chapters review some of the basic techniques, such as the fundamental epsilon-delta arguments, Taylor expansion, different types of convergence, and inequalities. The next five chapters discuss limit theorems in specific situations of observational data. Each of the first ten chapters contains at least one section of case study. The last six chapters are devoted to special areas of applications. This new edition introduces a final chapter dedicated to random matrix theory, as well as expanded treatment of inequalities and mixed effects models. The book's case studies and applications-oriented chapters demonstrate how to use methods developed from large sample theory in real world situations. The book is supplemented by a large number of exercises, giving readers opportunity to practice what they have learned. Appendices provide context for matrix algebra and mathematical statistics. The Second Edition seeks to address new challenges in data science. This text is intended for a wide audience, ranging from senior undergraduate students to researchers with doctorates. A first course in mathematical statistics and a course in calculus are prerequisites.
dc:type
Text
n10:P1001
n11:T1009
rdaw:P10219
2010
rdau:P60049
n5:1020