Asymptotics of improved generalized moment estimators for spatial autoregressive error models

Abstract

This article considers linear models with a spatial autoregressive error structure. Extending Arnold and Wied (2010) Arnold, M., Wied, D. (2010). Improved GMM estimation of the spatial autoregressive error model. Econ. Lett. 108:65–68.[Crossref], [Web of Science ®] , [Google Scholar], who develop an improved generalized method of moment (GMM) estimator for the parameters of the disturbance process to reduce the bias of existing estimation approaches, we establish the asymptotic normality of a new weighted version of this improved estimator and derive the efficient weighting matrix. We also show that this efficiently weighted GMM estimator is feasible as long as the regression matrix of the underlying linear model is non stochastic and illustrate the performance of the new estimator by a Monte Carlo simulation and an application to real data.     

Bootstrapping p Values and Power in the First-Order Autoregression: A Monte Carlo Investigation

The small-sample behavior of the bootstrap is investigated as a method for estimating p values and power in the stationary first-order autoregressive model. Monte Carlo methods are used to examine the bootstrap and Student-t approximations to the true distribution of the test statistic frequently used for testing hypotheses on the underlying slope parameter. In contrast to Student's t, the results suggest that the bootstrap can accurately estimate p values and power in this model in sample sizes as small as 5–10.     

On the Power of Bootstrapped Specification Tests

Abstract

Decisions based on econometric model estimates may not have the expected effect if the model is misspecified. Thus, specification tests should precede any analysis. Bierens' specification test is consistent and has optimality properties against some local alternatives. A shortcoming is that the test statistic is not distribution free, even asymptotically. This makes the test unfeasible. There have been many suggestions to circumvent this problem, including the use of upper bounds for the critical values. However, these suggestions lead to tests that lose power and optimality against local alternatives. In this paper we show that bootstrap methods allow us to recover power and optimality of Bierens' original test. Bootstrap also provides reliable p-values, which have a central role in Fisher's theory of hypothesis testing. The paper also includes a discussion of the properties of the bootstrap Nonlinear Least Squares Estimator under local alternatives.     

Testing for the Presence of a Cure Fraction in Clustered Interval‐Censored Survival Data

Clustered interval‐censored survival data are often encountered in clinical and epidemiological studies due to geographic exposures and periodic visits of patients. When a nonnegligible cured proportion exists in the population, several authors in recent years have proposed to use mixture cure models incorporating random effects or frailties to analyze such complex data. However, the implementation of the mixture cure modeling approaches may be cumbersome. Interest then lies in determining whether or not it is necessary to adjust the cured proportion prior to the mixture cure analysis. This paper mainly focuses on the development of a score for testing the presence of cured subjects in clustered and interval‐censored survival data. Through simulation, we evaluate the sampling distribution and power behaviour of the score test. A bootstrap approach is further developed, leading to more accurate significance levels and greater power in small sample situations. We illustrate applications of the test using data sets from a smoking cessation study and a retrospective study of early breast cancer patients.     

Bootstrap approximations for Bayesian analysis of Geo/G/1 discrete-time queueing models

In this paper we consider a Bayesian nonparametric approach to the analysis of discrete-time queueing models. The main motivation consists in applications to telecommunications, and in particular to asynchronous transfer mode (ATM) systems. Attention is focused on the posterior distribution of the overflow rate. Since the exact distribution of such a quantity is not available in a closed form, an approximation based on “proper” Bayesian bootstrap is proposed, and its properties are studied. Some possible alternatives to proper Bayesian bootstrap are also discussed. Finally, an application to real data is provided.     

A novel spatial outlier detection technique

Spatial outliers are spatially referenced objects whose non spatial attribute values are significantly different from the corresponding values in their spatial neighborhoods. In other words, a spatial outlier is a local instability or an extreme observation that deviates significantly in its spatial neighborhood, but possibly not be in the entire dataset. In this article, we have proposed a novel spatial outlier detection algorithm, location quotient (LQ) for multiple attributes spatial datasets, and compared its performance with the well-known mean and median algorithms for multiple attributes spatial datasets, in the literature. In particular, we have applied the mean, median, and LQ algorithms on a real dataset and on simulated spatial datasets of 13 different sizes to compare their performances. In addition, we have calculated area under the curve values in all the cases, which shows that our proposed algorithm is more powerful than the mean and median algorithms in almost all the considered cases and also plotted receiver operating characteristic curves in some cases.     

Assessing interactions for fixed‐dose drug combinations in subcutaneous tumor xenograft studies

Drug combinations in preclinical tumor xenograft studies are often assessed using fixed doses. Assessing the joint action of drug combinations with fixed doses has not been well developed in the literature. Here, an interaction index is proposed for fixed‐dose drug combinations in a subcutaneous tumor xenograft model. Furthermore, a bootstrap percentile interval of the interaction index is also developed. The joint action of two drugs can be assessed on the basis of confidence limits of the interaction index. Tumor xenograft data from actual two‐drug combination studies are analyzed to illustrate the proposed method. Copyright © 2013 John Wiley & Sons, Ltd.