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**Harvard**

Rootzén, H. och Zholud, D. (2016) *Tail estimation for window censored processes*.

** BibTeX **

@article{

Rootzén2016,

author={Rootzén, Holger and Zholud, Dmitrii},

title={Tail estimation for window censored processes},

journal={Technometrics},

issn={0040-1706},

volume={58},

issue={1},

abstract={This paper develops methods to estimate the tail and full distribution of the lengths of the 0-intervals in a continuous time stationary ergodic stochastic process which takes the values 0 and 1 in alternating intervals. The setting is that each of many such 0-1 processes have been observed during a short time window. Thus the observed 0-intervals could be non-censored, right censored, left censored or doubly censored, and the lengths of 0-intervals which are ongoing at the beginning of the observation window have a length-biased distribution. We exhibit parametric conditional maximum likelihood estimators for the full distribution, develop maximum likelihood tail estimation methods based on a semi-parametric generalized Pareto model, and propose goodness of fit plots. Finite sample properties are studied by simulation, and asymptotic normality is established for the most important case. The methods are applied to estimation of the length of off-road glances in the 100-car study, a big naturalistic driving experiment. Supplementary materials that include MatLab code for the estimation routines and a simulation study are available online.},

year={2016},

keywords={Generalized Pareto distribution, Length-biased distribution, Off-road glance, Tail estimation, Traffic safety, 100-car naturalistic driving study},

}

** RefWorks **

RT Journal Article

SR Electronic

ID 216893

A1 Rootzén, Holger

A1 Zholud, Dmitrii

T1 Tail estimation for window censored processes

YR 2016

JF Technometrics

SN 0040-1706

VO 58

IS 1

AB This paper develops methods to estimate the tail and full distribution of the lengths of the 0-intervals in a continuous time stationary ergodic stochastic process which takes the values 0 and 1 in alternating intervals. The setting is that each of many such 0-1 processes have been observed during a short time window. Thus the observed 0-intervals could be non-censored, right censored, left censored or doubly censored, and the lengths of 0-intervals which are ongoing at the beginning of the observation window have a length-biased distribution. We exhibit parametric conditional maximum likelihood estimators for the full distribution, develop maximum likelihood tail estimation methods based on a semi-parametric generalized Pareto model, and propose goodness of fit plots. Finite sample properties are studied by simulation, and asymptotic normality is established for the most important case. The methods are applied to estimation of the length of off-road glances in the 100-car study, a big naturalistic driving experiment. Supplementary materials that include MatLab code for the estimation routines and a simulation study are available online.

LA eng

DO 10.1080/00401706.2014.995834

LK http://dx.doi.org/10.1080/00401706.2014.995834

OL 30