Maximum likelihood estimation in the proportional hazards model of random censorship

The maximum likelihood estimator (MLE) for the survival function S_Tunder the proportional hazards model of censorship is derived and shown to differ from the Abdushukurov-Cheng-Lin estimator when the class of allowable distributions includes all continuous and discrete distributions. The estimators are compared via an example. The MLE is calculated using a Newton-Raphson iterative procedure and implemented via a FORTRAN algorithm.     

Estimation of Seemingly Unrelated Tobit Regressions via the EM Algorithm

An expectation-maximum (EM) likelihood algorithm is used to estimate two seemingly unrelated Tobit regressions in which the dependent variables are truncated normal. An illustrative example on the determination of the life-health insurance and pension benefits is also given.     

Conditional inference in finite population sampling under a calibration model

Several estimators, including the classical and the regression estimators of finite population mean, are compared, both theoretically and empirically, under a calibration model, where the dependent variable(y), and not the independent variable(x), can be observed for all units of the finite population. It is shown asymptotically that when conditioned on x, the bias of the classical estimator may be much smaller than that of the regression estimators; whereas when conditioned on y, the regression estimator may have much smaller conditional bias than the classical estimator. Since all the y's(not x's) can be observed, it seems appropriate to make comparison under the conditional distribution of each estimator with y fixed. In this case, the regression estimator has smaller variance, smaller conditional bias, and the conditional coverage probability closer to its nominal level     

The Identification of Multiple Outliers in ARIMA Models

Abstract

There are three main problems in the existing procedures for detecting outliers in ARIMA models. The first one is the biased estimation of the initial parameter values that may strongly affect the power to detect outliers. The second problem is the confusion between level shifts and innovative outliers when the series has a level shift. The third problem is masking. We propose a procedure that keeps the powerful features of previous methods but improves the initial parameter estimate, avoids the confusion between innovative outliers and level shifts and includes joint tests for sequences of additive outliers in order to solve the masking problem. A Monte Carlo study and one example of the performance of the proposed procedure are presented.     

Nonparametric Bayes and empirical Bayes estimation of the population median,with application in finite-population sampling

ABSTRACT

Nonparametric Bayes and empirical Bayes estimators of the population median are provided under Dirichlet process priors. The finite-population sampling is used to estimate the finite-population median under Dirichlet process priors. The asymptotic properties of the estimators are obtained from a frequentist perspective.     

On a class of shrinkage estimators of vector of regression coefficients the

This paper studies a class of shrinkage estimators of the vector of regression coefficients. The small disturbance approximations for the bias and the mean squared error matrix of the estimator are derived. In the sense of mean squared error, these estimators dominate the least squares estimator and the generalized Stein estimator developed by Hosmane (1988).     

Partitioned cross-validation

Partitioned cross-validation is proposed as a method for overcoming the large amounts of across sample variability to which ordinary cross-validation is subject. The price for cutting down on the sample noise is that a type of bias is intriduced. A theory is presented for optimal trade-off of this variance and bias. Comparison with other bandwidth selection methods is given.     

Estimation of tail parameters under type i censoring

This paper is a continuation of previous work concerning the estimation of tail-parameters under Type II censoring (Weissman 1978). The same estimation problem is considered here, this truip under Type I censoring. A sample of size n is censored below aE a given level x₀it is assumed that che underlying distriibution .function (df)belogs to the domain of attraction of a known extreme-value distribution and that K - K(x_o) , the number of observed values, remains finite as on - ∞ . We offer here estimators, which are asymptotically maximum likelihood estimators (MLE's), for quantiles associated with the tail of F such as location and scale parameters, quantiles and F(x) itself (for x in the tail). The results are applied to two illustrative examples.     

Cross‐Cultural Differences in Risk Perceptions of Disasters

Public risk perceptions of mass disasters carry considerable influences, both psychologically and economically, despite their oft‐times imprecise nature. Prior research has identified the presence of an optimistic bias that affects risk perception, but there is a dearth of literature examining how these perceptions differ among cultures—particularly with regard to mass disasters. The present study explores differences among Japanese, Argentinean, and North American mental health workers in their rates of the optimistic bias in risk perceptions as contrasted between natural disasters and terrorist events. The results indicate a significant difference among cultures in levels of perceived risk that do not correspond to actual exposure rates. Japanese groups had the highest risk perceptions for both types of hazards and North Americans and Argentineans had the lowest risk perceptions for terrorism. Additionally, participants across all cultures rated risk to self as lower than risk to others (optimistic bias) across all disaster types. These findings suggest that cultural factors may have a greater influence on risk perception than social exposure, and that the belief that one is more immune to disasters compared to others may be a cross‐cultural phenomenon.     

Further Results on Zellner's Minimum Expected Loss and Full Information Maximum Likelihood Estimators for Undersized Samples

The article presents a consistent set of conditions that a prior pdf for the reduced-form parameters must satisfy if Zellner's MELO estimators for the structural coefficients of a linear structural econometric model are to exist in all normal cases where the available sample is undersized. Also, the conditions under which the full information maximum likelihood estimators of structural coefficients exist are given. Finally, the article reports application of MELO estimation to Klein's Model I.