Askar H. Choudhury

Associations between Respiratory Illness and PM10 Air Pollution

In this study, the association between daily morbidity and respirable particulate pollution (i.e., particles with a mass median aerodynamic diameter of < or = 10 microns [PM10]) was evaluated in the general population of Anchorage, Alaska. Using insurance claims data for state employees and their dependents who lived in Anchorage, Alaska, the authors determined the number of medical visits for asthma, bronchitis, and upper respiratory infections. The number of visits were related to the level of particulate pollution in ambient air measured at air-monitoring sites. This study was conducted during a 3-y period, which included several weeks of higher-level particulate pollution that resulted from a volcanic eruption (i.e., August 1992). The particulate pollution was measured by the Anderson head sampler (24-h accumulation). The medical visits of the population at risk were also tallied daily. To help confirm whether PM10 exposure was a risk factor in the exacerbation of asthma, we used a regression analysis to regress daily asthma visits on PM10 pollution levels, controlling for seasonal variability. A significant positive association between morbidity and PM10 pollution was observed. The strongest association was with concurrent-day PM10 levels. The relative risk of morbidity was higher with respect to PM10 pollution during warmer days.

Understanding Time-Series Regression Estimators

Abstract A large number of methods have been developed for estimating time-series regression parameters. Students and practitioners have a difficult time understanding what these various methods are, let alone picking the most appropriate one for their application. This article explains how these methods are related. A chronology for the development of the various methods is presented, followed by a logical characterization of the methods. An examination of current computational techniques and computing power leads to the conclusion that exact maximum likelihood estimators should be used in almost all cases where regression models have autoregressive, moving average, or mixed autoregressive-moving average error structures.

A new approximate GLS estimator for the linear regression model with ARMA(p, q) disturbances

Abstract This paper introduces a new approximate GLS estimator for the linear regression model with ARMA( p , q ) disturbances, which involves a simple two-step transformation procedure and is readily implemented using any basic econometrics package.

A simplified GLS estimator for autoregressive moving-average models

Koreisha and Pukkila (1990a) have recently proposed a fast and efficient GLS estimator for the univariate ARMA time series model which appears to be far more robust than maximum likelihood methods and of comparable accuracy. The one drawback to this new estimator is that it requires use of the Cholesky decomposition. The purpose of this paper is to suggest an alternative simplified GLS estimator, which can be implemented with just repeated applications of an OLS subroutine. A limited Monte Carlo study establishes that this new estimator is just as efficient as that of Koreisha and Pukkila.

Linear estimation of the regression model with ARMA disturbances: a simulation study

Koreisha and Pukkila (1990a) have recently proposed a computationally convenient three-step GLS-type linear estimator for the regression model with ARMA disturbances involving three sequential applications of least squares. One potential drawback to this estimation procedure is that it entails dropping a significant number of initial observations. This paper uses Monte Carlo methods to evaluate its performance vis-a-vis existing OLS and GLS linear estimators.

Convenient Methods of Estimation of Linear Regression Models with MA( q) Disturbances

This note shows how G. M. MacDonald and J. G. MacKinnons (1985) convenient methods of estimating the linear regression model with MA(1) disturbances may be readily extended to the case of higher-order moving average disturbances. This involves a development of the key transformation, together with a simple detrimental result that facilitates the implementation of maximum likelihood estimation.

Untransformed first observation problem in regression model with moving average process

This paper shows the impact of underestimation of variance of an estima-tor when first observation is left untransformed to simplify the computational procedure. In fact, the bias of the variance is not diminishing even for large sample size for the model considered. By partitioning the covariance matrix into two parts, this paper explains why least square estimator with untrans-formed first observation shows such a consequence. To demonstrate this, an exact GLS estimator is developed by modifying an approximate estimator. Nonetheless, the computational simplicity remains same.

An alternative method for computing mean and covariance matrix of some multivariate distributions

Computing the mean and covariance matrix of some multivariate distributions, in particular, multivariate normal distribution and Wishart distribution are considered in this article. It involves a matrix transformation of the normal random vector into a random vector whose components are independent normal random variables, and then integrating univariate integrals for computing the mean and covariance matrix of a multivariate normal distribution. Moment generating function technique is used for computing the mean and covariances between the elements of a Wishart matrix. In this article, an alternative method that uses matrix differentiation and differentiation of the determinant of a matrix is presented. This method does not involve any integration.

THE CONSEQUENCES OF A POTENTIAL PITFALL IN APPROXIMATE GLS ESTIMATION OF THE REGRESSION MODEL WITH ARMA(P, Q) DISTURBANCES

This paper investigates the phenomenon of covariance matrix underestimation leading to possibly misleading inference which can arise from a potential pitfall in approximate GLS estimation of the regression model with ARMA disturbances.