Selection rules based on divergences

This paper deals with a special adaptive estimation problem, namely how can one select for each set of i.i.d. data X ₁, …, X _n the better of two given estimates of the data-generating probability density. Such a problem was studied by Devroye and Lugosi [Combinatorial Methods in Density Estimation, Springer, Berlin, 2001] who proposed a feasible suboptimal selection (called the Scheffé selection) as an alternative to the optimal but nonfeasible selection which minimizes the L₁-error. In many typical situations, the L₁-error of the Scheffé selection was shown to tend to zero for n→∞ as fast as the L₁-error of the optimal estimate. This asymptotic result was based on an inequality between the total variation errors of the Scheffé and optimal selections. The present paper extends this inequality to the class of φ-divergence errors containing the L₁-error as a special case. The first extension compares the φ-divergence errors of the mentioned Scheffé and optimal selections of Devroye and Lugosi. The second extension deals with a class of generalized Scheffé selections adapted to the φ-divergence error criteria and reducing to the classical Scheffé selection for the L₁-criterion. It compares the φ-divergence errors of these feasible selections and the optimal nonfeasible selections minimizing the φ-divergence errors. Both extensions are motivated and illustrated by examples.     

Experience with a criterion for the classification of johnson distributions

We use simulation to investigate the choice of percentiles when using the criterion given by Bowman and Shenton (1989) to select between Johnson's SB and STJ distributions for a data set. We also look at the choice of a tolerance level in classifying distributions as Johnson's SL-     

Threshold Estimation via Group Orthogonal Greedy Algorithm

A threshold autoregressive (TAR) model is an important class of nonlinear time series models that possess many desirable features such as asymmetric limit cycles and amplitude-dependent frequencies. Statistical inference for the TAR model encounters a major difficulty in the estimation of thresholds, however. This article develops an efficient procedure to estimate the thresholds. The procedure first transforms multiple-threshold detection to a regression variable selection problem, and then employs a group orthogonal greedy algorithm to obtain the threshold estimates. Desirable theoretical results are derived to lend support to the proposed methodology. Simulation experiments are conducted to illustrate the empirical performances of the method. Applications to U.S. GNP data are investigated.     

Design considerations for small experiments and simple logistic regression

Inference for a generalized linear model is generally performed using asymptotic approximations for the bias and the covariance matrix of the parameter estimators. For small experiments, these approximations can be poor and result in estimators with considerable bias. We investigate the properties of designs for small experiments when the response is described by a simple logistic regression model and parameter estimators are to be obtained by the maximum penalized likelihood method of Firth [Firth, D., 1993, Bias reduction of maximum likelihood estimates. Biometrika, 80, 27–38]. Although this method achieves a reduction in bias, we illustrate that the remaining bias may be substantial for small experiments, and propose minimization of the integrated mean square error, based on Firth's estimates, as a suitable criterion for design selection. This approach is used to find locally optimal designs for two support points.     

Analysis of autoregressive models with symmetric stable innovations

This paper develops algorithms for fitting autoregressive models with symmetric stable innovations using auto-covariation function. A recursive algorithm is proposed for generalized Yule-Walker estimation of autoregressive coefficients and partial auto-covariation function. It also introduces a new information criterion, useful for consistent order selection. Applications of the proposed methods are illustrated using observations simulated from autoregressive models with symmetric stable innovations as well as by analysing a set of real data.     

Using quantile plots of the prediction variance for comparing designs for a constrained mixture region: an application involving a fertilizer experiment

Summary. Vining and co-workers have used plots of the prediction variance trace (PVT) along the so-called prediction rays to compare mixture designs in a constrained region R . In the present paper, we propose a method for describing the distribution of the prediction variance within the region R by using quantile plots. More comprehensive comparisons between mixture designs are possible through the proposed plots than with the PVT plots. The utility of the quantile plots is illustrated with a four-component fertilizer experiment that was initiated in São Paulo, Brazil.     

论音系学的形式主义与功能主义

形式主义和功能主义是当今语言学的两大派系,因而对音系学的分析可以从形式主义和功能主义两方面来着手。音系学理论的发展离不开功能主义,尤其是功能主义优选论的产生,给形式主义抹上了浓重的功能主义的色彩;功能主义的解释也在优选论的制约条件层级排列中,得到了形式主义的表述,从而更加促进了形式主义和功能主义在音系学理论框架内的融合。