MARC 主机 00000cam a2201501 i 4500
001 ocn907885357
003 OCoLC
005 20151106090903.2
008 150515s2015 nju b 001 0 eng c
010 2015017266
020 9789814675628
020 9814675628
035 (OCoLC)907885357
040 OU/DLC|beng|erda|cOSU|dDLC|dYDXCP|dBTCTA|dCDX|dOCLCF|dIUL
|dNhCcYME
042 pcc
049 RBNN
050 00 QA273.67|b.W36 2015
090 QA273.67|b.W36 2015
100 1 Wang, Qiying,|eauthor.
245 10 Limit theorems for nonlinear cointegrating regression /
|cQiying Wang, The University of Sydney, Australia.
264 1 New Jersey :|bWorld Scientific,|c[2015]
300 x, 261 pages ;|c24 cm.
336 text|btxt|2rdacontent.
337 unmediated|bn|2rdamedia.
338 volume|bnc|2rdacarrier.
490 1 Nonlinear time series and chaos ;|vvol. 5.
504 Includes bibliographical references and index.
650 0 Limit theorems (Probability theory)
650 0 Nonlinear systems.
830 0 Nonlinear time series and chaos ;|vvol. 5.
907 .b7652128x|b04-12-16|c10-02-15
910 OCLC BibNote|bMaster record encoding level change
910 RDA ENRICHED
910 ybp
910 Backstage
910 TOC
910 Hathi Trust report SPM
910 BROWNu
970 01 |tPreface|pv
970 01 |l1.|tIntroduction|p1
970 01 |l2.|tConvergence to local time|p5
970 11 |l2.1.|tLocal time: definition and existence|p6
970 11 |l2.1.1.|tLocal time of Gaussian process|p8
970 11 |l2.1.2.|tLocal time of Levy process|p11
970 11 |l2.1.3.|tLocal time of semimartingale|p12
970 11 |l2.2.|tConvergence to integral functionals of stochastic
processes|p17
970 11 |l2.2.1.|tExistence of integral10g(Xs)ds|p18
970 11 |l2.2.2.|tConvergence to integral10g(Xs)ds|p21
970 11 |l2.2.3.|tSupplement and generalization|p23
970 11 |l2.3.|tConvergence to local time|p27
970 11 |l2.3.1.|tStrong smooth array: definition and examples|p28
970 11 |l2.3.2.|tConvergence to local time: framework I|p32
970 11 |l2.3.3.|tExample: linear processes|p38
970 11 |l2.3.4.|tProofs of Corollaries 2.3 and 2.4|p41
970 11 |l2.3.5.|tConvergence to local time: framework II|p47
970 11 |l2.3.6.|tExample: stationary processes|p55
970 11 |l2.4.|tConvergence to self-intersection local time|p60
970 11 |l2.5.|tUniform approximation to local time|p63
970 11 |l2.5.1.|tAn invariance principle|p64
970 11 |l2.5.2.|tUniform approximation to local time|p67
970 11 |l2.5.3.|tUniform approximation to local time: random
bandwidth|p68
970 11 |l2.5.4.|tExample: linear processes|p71
970 11 |l2.5.5.|tProofs of main results|p72
970 11 |l2.6.|tBibliographical Notes|p78
970 01 |l3.|tConvergence to a mixture of normal distributions|p81
970 11 |l3.1.|tConvergence on product space|p81
970 11 |l3.2.|tConvergence to a process with stationary
independent increments|p85
970 11 |l3.2.1.|tCentral limit theorem for dependent random
variables|p86
970 11 |l3.2.2.|tFunctional central limit theorems for
martingales|p90
970 11 |l3.2.3.|tMultivariate martingale limit theorem|p92
970 11 |l3.2.4.|tConvergence to a stable Levy process|p94
970 11 |l3.3.|tConvergence to a mixture of normal distributions:
martingale arrays|p96
970 11 |l3.3.1.|tA framework|p96
970 11 |l3.3.2.|tLimit theorem for martingale: under conditional
variance condition (CVC)|p97
970 11 |l3.3.3.|tExamples and remarks|p101
970 11 |l3.4.|tMartingale limit theorem revisited|p103
970 11 |l3.4.1.|tLimit theorem for martingale: under convergence
in distribution for conditional variance|p104
970 11 |l3.4.2.|tTwo examples|p106
970 11 |l3.4.3.|tProof of Theorem 3.13|p111
970 11 |l3.5.|tConvergence to a mixture of normal distributions:
beyond martingale arrays|p119
970 11 |l3.5.1.|tProof of Theorem 3.16|p123
970 11 |l3.5.2.|tSome subsidiary propositions|p125
970 11 |l3.6.|tConvergence to a mixture of normal distributions:
zero energy functionals|p130
970 11 |l3.7.|tUniform convergence for a class of martingales
|p132
970 11 |l3.7.1.|tA framework and applications|p132
970 11 |l3.7.2.|tRemarks and extension|p137
970 11 |l3.7.3.|tExamples: Identification of (3.125)|p140
970 11 |l3.8.|tLimit theorems for continuous time martingales
|p145
970 11 |l3.8.1.|tConvergence to a continuous Gaussian process
|p145
970 11 |l3.8.2.|tConvergence to a mixture of Gaussian processes
|p146
970 11 |l3.9.|tBibliographical Notes|p148
970 01 |l4.|tConvergence to stochastic integrals|p151
970 11 |l4.1.|tDefinition of stochastic integrals|p151
970 11 |l4.1.1.|tStochastic integrals with respect to a Brownian
motion|p152
970 11 |l4.1.2.|tStochastic integrals with respect to a (local)
square integrable martingale|p154
970 11 |l4.1.3.|tStochastic integrals with respect to a local
(semi-) martingale|p155
970 11 |l4.1.4.|tMultivariate stochastic integrals|p156
970 11 |l4.1.5.|tProperties of stochastic integrals|p157
970 11 |l4.1.6.|tMartingale representation theorem and It©þ's
formula|p158
970 11 |l4.2.|tWeak convergence of stochastic integrals|p159
970 11 |l4.3.|tWeak convergence of stochastic integrals:
multivariate extension|p161
970 11 |l4.4.|tConvergence to stochastic integrals: random arrays
|p162
970 11 |l4.5.|tConvergence to stochastic integrals: beyond the
semi-martingale|p165
970 11 |l4.5.1.|tLPWW decomposition theorem|p166
970 11 |l4.5.2.|tLong memory processes|p170
970 11 |l4.5.3.|tShort memory processes|p174
970 11 |l4.5.4.|tLPWW decomposition theorem: multivariate
extension|p179
970 11 |l4.5.5.|tExtension to a-mixing sequences|p181
970 11 |l4.6.|tBibliographical Notes|p186
970 01 |l5.|tNonlinear cointegrating regression|p189
970 11 |l5.1.|tNonparametric estimation|p189
970 11 |l5.1.1.|tNadaraya-Watson estimator|p190
970 11 |l5.1.2.|tNadaraya-Watson estimator: certain extensions
|p195
970 11 |l5.1.3.|tBias analysis and local linear estimator|p199
970 11 |l5.1.4.|tUniform convergence for local linear estimator
|p202
970 11 |l5.1.5.|tMultivariate contingrating regression|p207
970 11 |l5.2.|tParametric estimation|p210
970 11 |l5.2.1.|tWeak consistency|p210
970 11 |l5.2.2.|tAsymptotic distribution|p215
970 11 |l5.3.|tModel specification testing|p219
970 11 |l5.4.|tBibliographical Notes|p225
970 01 |tAppendix A Concepts of stochastic processes|p229
970 01 |tAppendix B Metric space|p237
970 01 |tAppendix C Convergence of probability measure|p241
970 01 |tBibliography|p245
970 01 |tIndex|p257
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