Estimating the distribution of one-dimensional discrete scan statistics viewed as extremes of 1-dependent stationary sequences

DOI10.1016/j.jspi.2006.06.010zbMath1119.62092OpenAlexW1991045459MaRDI QIDQ866625

George Haiman

Publication date: 14 February 2007

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2006.06.010

zbMATH Keywords

approximation methodslongest success runcharge problemdistributions of extremesone-dependent stationaryscan statistics generated by Bernoulli random variables

Mathematics Subject Classification ID

Statistics of extreme values; tail inference (62G32) Probability distributions: general theory (60E05) Markov chains (discrete-time Markov processes on discrete state spaces) (60J10) Inference from stochastic processes (62M99)

Related Items (12)

Scan statistics for detecting a local change in variance for normal data with unknown population variance ⋮ Scan statistics for detecting a local change in variance for normal data with known variance ⋮ A new method of approximating the probability of matching common words in multiple random sequences ⋮ One dimensional scan statistics generated by some dependent stationary sequences ⋮ Approximations and inequalities for moving sums ⋮ Approximations for two-dimensional discrete scan statistics in some block-factor type dependent models ⋮ Detection and analysis of spikes in a random sequence ⋮ Robust scan statistics for detecting a local change in population mean for normal data ⋮ Scan statistics for normal data with outliers ⋮ Discrete Scan Statistics Generated by Exchangeable Binary Trials ⋮ Scan statistics for monitoring data modeled by a negative binomial distribution ⋮ Variable Window Scan Statistics for Normal Data

Cites Work

• Estimation for the distribution of two-dimensional discrete scan statistics
• A new method for estimating the distribution of scan statistics for a two-dimensional Poisson process
• On the length of the longest run in a multi-state Markov chain.
• First passage time for some stationary processes
• Estimating the distributions of scan statistics with high precision
• Approximations for Distributions of Scan Statistics
• On Runs and Longest Run Tests: A Method of Finite Markov Chain Imbedding
• On exact and large deviation approximation for the distribution of the longest run in a sequence of two-state Markov dependent trials
• Distribution of the scan statistic for a sequence of bistate trials
• ON THE POWER FUNCTION OF THE LONGEST RUN AS A TEST FOR RANDOMNESS IN A SEQUENCE OF ALTERNATIVES
• Scan statistics

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