000 04330nam a22005775i 4500
001 978-0-387-26269-7
003 DE-He213
005 20180115171351.0
007 cr nn 008mamaa
008 100301s2005 xxu| s |||| 0|eng d
020 _a9780387262697
_9978-0-387-26269-7
024 7 _a10.1007/b100107
_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 _aWu, Yanhong.
_eauthor.
245 1 0 _aInference for Change Point and Post Change Means After a CUSUM Test
_h[electronic resource] /
_cby Yanhong Wu.
264 1 _aNew York, NY :
_bSpringer New York,
_c2005.
300 _aXIII, 158 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Statistics,
_x0930-0325 ;
_v180
505 0 _aCUSUM Procedure -- Change-Point Estimation -- Confidence Interval for Change-Point -- Inference for Post-Change Mean -- Estimation After False Signal -- Inference with Change in Variance -- Sequential Classification and Segmentation -- An Adaptive CUSUM Procedure -- Dependent Observation Case -- Other Methods and Remarks.
520 _aThis monograph is the first to systematically study the bias of estimators and construction of corrected confidence intervals for change-point and post-change parameters after a change is detected by using a CUSUM procedure. Researchers in change-point problems and sequential analysis, time series and dynamic systems, and statistical quality control will find that the methods and techniques are mostly new and can be extended to more general dynamic models where the structural and distributional parameters are monitored. Practitioners, who are interested in applications to quality control, dynamic systems, financial markets, clinical trials and other areas, will benefit from case studies based on data sets from river flow, accident interval, stock prices, and global warming. Readers with an elementary probability and statistics background and some knowledge of CUSUM procedures will be able to understand most results as the material is relatively self-contained. The exponential family distribution is used as the basic model that includes changes in mean, variance, and hazard rate as special cases. There are fundamental differences between the sequential sampling plan and fixed sample size. Although the results are given under the CUSUM procedure, the methods and techniques discussed provide new approaches to deal with inference problems after sequential change-point detection, and they also contribute to the theoretical aspects of sequential analysis. Many results are of independent interests and can be used to study random walk related stochastic models. Yanhong Wu is a visiting lecturer in statistics at the University of the Pacific. Previously, he was a visiting associate professor at the University of Michigan and an assistant professor at the University of Alberta. He has published more than forty research papers on the topics of change-point problem, quality control, mixture models, risk theory, and reliability mathematics. He was the receiver of Pierre-Robillard Award from the Canadian Statistical Society. .
650 0 _aMathematics.
650 0 _aProbabilities.
650 0 _aStatistics.
650 0 _aQuality control.
650 0 _aReliability.
650 0 _aIndustrial safety.
650 0 _aEconometrics.
650 1 4 _aMathematics.
650 2 4 _aProbability Theory and Stochastic Processes.
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aQuality Control, Reliability, Safety and Risk.
650 2 4 _aStatistics for Business/Economics/Mathematical Finance/Insurance.
650 2 4 _aEconometrics.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9780387229270
830 0 _aLecture Notes in Statistics,
_x0930-0325 ;
_v180
856 4 0 _uhttp://dx.doi.org/10.1007/b100107
912 _aZDB-2-SMA
999 _c369298
_d369298