A note on the central limit theorem for the bootstrap mean

Following Yang (1988), a simple, more self-contained derivation of the asymptotic normality of the bootstrap mean is presented, as well as other asymptotic results. The derivations are appropriate for beginning graduate students in statistics, relying only on fundamental notions of probability theory and analysis,     

Improved bounds for moments of some rank statistics

Improved lower hounds are obtained for two parameters μ(F) and λ(F) which occur in the moments of linear rank statistics studied by Doksum, Hollander, Lehmann and Hsu. IT is shown that if F is a continuous distribution, then μ(F) > 3/10 and λ(F) >= 89/315 - 0.28254. These results decrease the range of possible values of μ(F) and λ(F) by more than 84% and 34%, respectively, over those previously reported.     

Correcting for nonlinear measurement errors in the dependent variable in the general linear model

Linear models are considered in which measurement error is present in the dependent variable. Observed values are related to true values via nonlinear regression models with the parameters in the measurement error models being estimated with the use of independent, external data, collected using standards. Pseudo-maximum likelihood estimators and their asymptotic properties are developed under normality assumptions and the common approach of simply analyzing imputed values obtained from the nestimated calibration curves is assessed. A small simulation evaluates the procedures. An example is presented in which urinary neopterin (measured via radioimmunoassay) is nbeing compared between two groups of individuals.     

Extension to higher dimensions of the jaeschke-eicker result on the standardized empirical process

The asymptotic distribution of the sup-norm of the heavily weighted empirical process is established in the multidimensional case. This theorem extends in particular the famous result in Jaeschke (1975, 1979) to higher dimensions. There is a striking difference between the behaviour for higher dimensions and that for dimension one, especially the limiting distribution is now a simple transformation of a standard exponential random variable.     

A note on robustness of D-optimal block designs for two-colour microarray experiments

Two-colour microarray experiments form an important tool in gene expression analysis. Due to the high risk of missing observations in microarray experiments, it is fundamental to concentrate not only on optimal designs but also on designs which are robust against missing observations. As an extension of Latif et al. (2009), we define the optimal breakdown number for a collection of designs to describe the robustness, and we calculate the breakdown number for various D-optimal block designs. We show that, for certain values of the numbers of treatments and arrays, the designs which are D-optimal have the highest breakdown number. Our calculations use methods from graph theory.     

Indexing the Federal Tax System: A Cost-of-Living Approach

In this article, we analyze the indexation of federal taxes, using an approach based on cost-of-living measurement. We use our Tax and Price Index methodology and data base to study an indexed system historically, comparing indexation with the Consumer Price Index (CPI) to actual tax policy, a tax system with constant parameters, and an “exact” indexing scheme. We reach three main conclusions: (a) The sequence of tax reductions implemented between 1967 and 1985 have fallen short of mimicking indexation, (b) wealthier households would have benefited relatively more than lower-income households from indexation, and (c) CPI indexation would not have completely eliminated bracket creep.     

How Do Behavioral Assumptions Affect Structural Inference? Evidence From a Laboratory Experiment

We develop a model of labor force status (federal employment, nonfederal employment, unemployment, and out of the labor force) that depends on human capital variables, local labor market conditions, and personal characteristics. According to the estimated model for white non-Hispanic males and females a substantial difference exists between blacks and white non-Hispanics even after correction for the control variables. However, the control variables explain almost all of the difference between Hispanics and white non-Hispanics.     

Some Observations,a Suggestion,and Some Comments on the Conway-Roberts Article

This article provides new tools for the evaluation of dynamic stochastic general equilibrium (DSGE) models and applies them to a large-scale new Keynesian model. We approximate the DSGE model by a vector autoregression, and then systematically relax the implied cross-equation restrictions and document how the model fit changes. We also compare the DSGE model's impulse responses to structural shocks with those obtained after relaxing its restrictions. We find that the degree of misspecification in this large-scale DSGE model is no longer so large as to prevent its use in day-to-day policy analysis, yet is not small enough to be ignored.     

Flexible Bivariate Count Data Regression Models

The article develops a semiparametric estimation method for the bivariate count data regression model. We develop a series expansion approach in which dependence between count variables is introduced by means of stochastically related unobserved heterogeneity components, and in which, unlike existing commonly used models, positive as well as negative correlations are allowed. Extensions that accommodate excess zeros, censored data, and multivariate generalizations are also given. Monte Carlo experiments and an empirical application to tobacco use confirms that the model performs well relative to existing bivariate models, in terms of various statistical criteria and in capturing the range of correlation among dependent variables. This article has supplementary materials online.     

A fast algorithm for non-negativity model selection

An efficient optimization algorithm for identifying the best least squares regression model under the condition of non-negative coefficients is proposed. The algorithm exposits an innovative solution via the unrestricted least squares and is based on the regression tree and branch-and-bound techniques for computing the best subset regression. The aim is to filling a gap in computationally tractable solutions to the non-negative least squares problem and model selection. The proposed method is illustrated with a real dataset. Experimental results on real and artificial random datasets confirm the computational efficacy of the new strategy and demonstrates its ability to solve large model selection problems that are subject to non-negativity constrains.