000  04330nam a22005775i 4500  

001  9780387262697  
003  DEHe213  
005  20180115171351.0  
007  cr nn 008mamaa  
008  100301s2005 xxu s  0eng d  
020 
_a9780387262697 _99780387262697 

024  7 
_a10.1007/b100107 _2doi 

050  4  _aQA273.A1274.9  
050  4  _aQA274274.9  
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, _x09300325 ; _v180 

505  0  _aCUSUM Procedure  ChangePoint Estimation  Confidence Interval for ChangePoint  Inference for PostChange 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 changepoint and postchange parameters after a change is detected by using a CUSUM procedure. Researchers in changepoint 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 selfcontained. 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 changepoint 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 changepoint problem, quality control, mixture models, risk theory, and reliability mathematics. He was the receiver of PierreRobillard 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, _x09300325 ; _v180 

856  4  0  _uhttp://dx.doi.org/10.1007/b100107 
912  _aZDB2SMA  
999 
_c369298 _d369298 