Seasonal warranty prediction based on recurrent event data
DOI10.1214/20-AOAS1333zbMath1446.62322MaRDI QIDQ2194479
Qianqian Shan, William Q. Meeker, Yili Hong
Publication date: 26 August 2020
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.aoas/1593449332
EM algorithmhierarchical clusteringrandom effectsmissing datanonhomogeneous Poisson process (NHPP)seasonal dynamic covariates
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics in engineering and industry; control charts (62P30) Point processes (e.g., Poisson, Cox, Hawkes processes) (60G55) Missing data (62D10)
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Cites Work
- Algorithm AS 136: A K-Means Clustering Algorithm
- Models and estimation for systems with recurrent events and usage processes
- Bayesian reliability
- Point processes and queues. Martingale dynamics
- Alternative time scales and failure time models
- Modeling for seasonal marked point processes: an analysis of evolving hurricane occurrences
- The statistical analysis of recurrent events.
- Nonparametric Estimation from Incomplete Observations
- Calibrating Prediction Regions
- Frequentist prediction intervals and predictive distributions
- Regression Methods for Poisson Process Data
- Finding Groups in Data
- Least squares quantization in PCM
- Recurrent Events Data Analysis for Product Repairs, Disease Recurrences, and Other Applications
- Some Simple Robust Methods for the Analysis of Recurrent Events
- An Introduction to Statistical Learning
- Calibrating predictive distributions
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