000 03268nam a22004575i 4500
001 978-3-0348-0240-6
003 DE-He213
005 20140220083253.0
007 cr nn 008mamaa
008 111104s2012 sz | s |||| 0|eng d
020 _a9783034802406
_9978-3-0348-0240-6
024 7 _a10.1007/978-3-0348-0240-6
_2doi
050 4 _aQA273.A1-274.9
050 4 _aQA274-274.9
072 7 _aPBT
_2bicssc
072 7 _aPBWL
_2bicssc
072 7 _aMAT029000
_2bisacsh
082 0 4 _a519.2
_223
100 1 _aPrakasa Rao, B.L.S.
_eauthor.
245 1 0 _aAssociated Sequences, Demimartingales and Nonparametric Inference
_h[electronic resource] /
_cby B.L.S. Prakasa Rao.
264 1 _aBasel :
_bSpringer Basel,
_c2012.
300 _aXII, 272 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aProbability and its Applications
505 0 _aPreface -- Associated Random Variables and Related Concepts -- Demimartingales -- N-Demimartingales -- Conditional Demimartingales -- Multidimensionally Indexed Demimartingales and Continuous Parameter Demimartingales -- Limit Theorems for Associated Random Variables -- Nonparametric Estimation for Associated Sequences -- Nonparametric Tests for Associated Sequences -- Nonparametric Tests for Change in Marginal Density Function for Associated Sequences -- References -- Index.
520 _aThis book gives a comprehensive review of results for associated sequences and demimartingales developed so far, with special emphasis on demimartingales and related processes.   One of the basic aims of theory of probability and statistics is to build stochastic models which explain the phenomenon under investigation and explore the dependence among various covariates which influence this phenomenon. Classic examples are the concepts of Markov dependence or of mixing for random processes. Esary, Proschan and Walkup introduced the concept of association for random variables, and Newman and Wright studied properties of processes termed as demimartingales. It can be shown that the partial sums of mean zero associated random variables form a demimartingale.   Probabilistic properties of associated sequences, demimartingales and related processes are discussed in the first six chapters. Applications of some of these results to problems in nonparametric statistical inference for such processes are investigated in the last three chapters.   This book will appeal to graduate students and researchers interested in probabilistic aspects of various types of stochastic processes and their applications in reliability theory, statistical mechanics, percolation theory and other areas.
650 0 _aMathematics.
650 0 _aDistribution (Probability theory).
650 1 4 _aMathematics.
650 2 4 _aProbability Theory and Stochastic Processes.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783034802390
830 0 _aProbability and its Applications
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-0348-0240-6
912 _aZDB-2-SMA
999 _c101719
_d101719