Journal article
Quasi-Likelihood Estimation of a Censored Autoregressive Model With Exogenous Variables
Journal of the American Statistical Association, Vol.113(523), pp.1135-1145
07/03/2018
DOI: 10.1080/01621459.2017.1307115
Abstract
Maximum likelihood estimation of a censored autoregressive model with exogenous variables (CARX) requires computing the conditional likelihood of blocks of data of variable dimensions. As the random block dimension generally increases with the censoring rate, maximum likelihood estimation becomes quickly numerically intractable with increasing censoring. We introduce a new estimation approach using the complete-incomplete data framework with the complete data comprising the observations were there no censoring. We introduce a system of unbiased estimating equations motivated by the complete-data score vector, for estimating a CARX model. The proposed quasi-likelihood method reduces to maximum likelihood estimation when there is no censoring, and it is computationally efficient. We derive the consistency and asymptotic normality of the quasi-likelihood estimator, under mild regularity conditions. We illustrate the efficacy of the proposed method by simulations and a real application on phosphorus concentration in river water.
Details
- Title: Subtitle
- Quasi-Likelihood Estimation of a Censored Autoregressive Model With Exogenous Variables
- Creators
- Chao Wang - Department of Statistics and Actuarial Science, The University of IowaKung-Sik Chan - Department of Statistics and Actuarial Science, The University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of the American Statistical Association, Vol.113(523), pp.1135-1145
- DOI
- 10.1080/01621459.2017.1307115
- ISSN
- 0162-1459
- eISSN
- 1537-274X
- Publisher
- Taylor & Francis
- Language
- English
- Date published
- 07/03/2018
- Academic Unit
- Statistics and Actuarial Science; Radiology
- Record Identifier
- 9983985854402771
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