Likelihood Inference in Small-Area Estimation Using P-Spline and Time Series Models

Mahmoud Torabi, Farhad Shokoohi: Non-parametric Small Area Estimation. The Joint Statistical Meeting 2013, Montreal, QC, Canada, 2013, (Invited Talk).

Abstract

Mixed models are commonly used for the analysis of small area estimation. In particular, small area estimation has been extensively studied under linear mixed models. Recently, small area estimation under the linear mixed model with penalized spline model, for fixed part of the model, was proposed. However, in practice there are many situations that we have counts or proportions in small area estimation as a response variable; for example a dataset on the number of incidences in small areas. In this talk, small area estimation under generalized linear mixed models using penalized spline mixed models is proposed. A likelihood-based approach is used to predict small area parameters and also to provide prediction intervals. The performance of the proposed approach is evaluated through simulation studies and also by real datasets.

BibTeX (Download)

@conference{Shokoohi2013JSM,
title = {Non-parametric Small Area Estimation},
author = {Mahmoud Torabi and Farhad Shokoohi},
url = {https://ww2.amstat.org/meetings/jsm/2013/onlineprogram/AbstractDetails.cfm?abstractid=307865},
year  = {2013},
date = {2013-08-08},
address = {The Joint Statistical Meeting 2013, Montreal, QC, Canada},
abstract = {Mixed models are commonly used for the analysis of small area estimation. In particular, small area estimation has been extensively studied under linear mixed models. Recently, small area estimation under the linear mixed model with penalized spline model, for fixed part of the model, was proposed. However, in practice there are many situations that we have counts or proportions in small area estimation as a response variable; for example a dataset on the number of incidences in small areas. In this talk, small area estimation under generalized linear mixed models using penalized spline mixed models is proposed. A likelihood-based approach is used to predict small area parameters and also to provide prediction intervals. The performance of the proposed approach is evaluated through simulation studies and also by real datasets.},
note = {Invited Talk},
keywords = {Asthma, Bayesian computation, Nonparametric models, Small-area estimation},
pubstate = {published},
tppubtype = {conference}
}