Open Access
Scan Statistics for Normal Data
Xiao Wang
- 01 Jan 2013
TL;DR: In this dissertation, accurate approximations and inequalities for the distribution of fixed window scan statistics for observations from a continuous model are derived using the R algorithms for multivariate normal and t probabilities developed by Genz and Bretz (2009), which are utilized to investigate the performance ofFixed window scan Statistics for detecting a local shift in the process mean for iid normal data.
read more
Abstract: In this dissertation we derive accurate approximations and inequalities for the distribution of fixed window scan statistics for observations from a continuous model. Employing the R algorithms for multivariate normal and t probabilities developed by Genz and Bretz (2009), these approximations and inequalities are applied to normal observations, with mean and variance being both known and unknown. These approximations are utilized to investigate the performance of fixed window scan statistics for detecting a local shift in the process mean for iid normal data. Both one and two dimensional scan statistics are investigated. To detect a local change of unknown size in the process mean, a multiple window scan statistic is introduced and compared with fixed window scan statistics via a power comparison. These results are also extended to ARMA time series data, which consists of dependant observations. It is concluded that both approximations and inequalities are quite accurate, and when the size of a local change in the process mean is unknown, the multiple window scan statistic outperforms fixed window scan statistics. Scan Statistics for Normal Data
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
•Dissertation
Approximations for multidimensional discrete scan statistics
Alexandru Amărioarei
- 15 Sep 2014
TL;DR: This thesis improves some existing results concerning the estimation of the distribution of extremes of 1-dependent stationary sequences of random variables, and derives accurate approximations and error bounds for the probability distribution of the multidimensional discrete scan statistics.
11
References
•Book
Time series analysis, forecasting and control
George E. P. Box,Gwilym M. Jenkins +1 more
- 01 Jan 1970
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
19.7K
Time Series Analysis: Forecasting and Control
TL;DR: Time Series Analysis and Forecasting: principles and practice as mentioned in this paper The Oxford Handbook of Quantitative Methods, Vol. 3, No. 2: Statistical AnalysisTime-Series ForecastingPractical Time-Series AnalysisApplied Bayesian Forecasting and Time Series AnalysisSAS for Forecasting Time SeriesApplied Time Series analysisTime Series analysisElements of Nonlinear Time Series analyses and forecastingTime series analysis and forecasting by Example.
19.6K
Time series analysis, forecasting and control
P. Young,S. Shellswell +1 more
TL;DR: Time series analysis san francisco state university, 6 4 introduction to time series analysis, box and jenkins time seriesAnalysis forecasting and, th15 weeks citation classic eugene garfield, proc arima references 9 3 sas support, time series Analysis forecasting and control pambudi, timeseries analysis forecasting and Control george e.
14.1K
•Book
Statistics for spatial data
Noel A Cressie,Noel A Cressie +1 more
- 01 Jan 1991
TL;DR: In this paper, the authors present a survey of statistics for spatial data in the field of geostatistics, including spatial point patterns and point patterns modeling objects, using Lattice Data and spatial models on lattices.
9K