Errors of misclassification associated with the inverse gaussion distribution

Errors of misclassification and their probabilities are studied for classification problems associated with univariate inverse Gaussian distributions. The effects of applying the linear discriminant function (LDF), based on normality, to inverse Gaussian populations are assessed by comparing probabilities (optimum and conditional) based on the LDF with those based on the likelihood ratio rule (LR) for the inverse Gaussian, Both theoretical and empirical results are presented     

Nonsampling errors in surveys - a review

Response errors have become extremely important in increasingly complex surveys and a review of the ever expanding literature on the subject was judged necessary. The emphasis here is on models for response and nonresponse errors the most recent ones incorporating the concept of response probabilities.     

Coefficients of determinations for variable selection in the msae regression

Our objective is to modify a robust coefficient of determination for the minimum sum of absolute errors MSAE regression proposed by McKean and Sievers (1987) so that it satisfies all the desirable properties. We also propose an adjusted coefficient of determination that is appropriate for comparing several models with different number of variables. Further, it has the property that if it decreases with the addition of predictor variables to the model, then the contribution of these variables is statistically non-significant. We illustrate the results with an example.     

Corrected f-tests in the general linear model

The theory of corrected F-tests in the general linear modeL with correlated errors is studied in this paper. Strictly unbiased F-tests are constructed in the presence of a large class of known non-identity correlation structures. These are generalized likelihood ratio tests which are easily computed. Critical points from the standard F-tables can be used to provide an exact test of desired size.     

Statistical Artifacts in the Ratio of Discrete Quantities

The ratio is a familiar statistic, but it is often misused. One frequently overlooked problem occurs when ratioing two discrete (digitized) variables. Fine structure appears in the histogram of the ratio that can be very subtle, or can sometimes even dominate the histogram. It disappears when the numerator and/or denominator become continuous. This statistical artifact is not a binning error, nor is it removed by taking more data. It is important to be aware of the artifact in order to avoid misinterpretation of ratio data. We provide examples of the statistical artifact (including one from baseball) and discuss ways to avoid or minimize the problems it can cause.     

Analysis of mixed outcomes: misclassified binary responses and measurement error in covariates

This paper considers regression models for mixed binary and continuous outcomes, when the true predictor is measured with error and the binary responses are subject to classification errors. The focus of the paper is to study the effects of these errors on the estimates of the model parameters and also to propose a model that incorporates both these errors. The proposed model results in a substantial improvement in the estimates as shown by extensive simulation studies.     

The widespread misinterpretation of p-values as error probabilities

The anonymous mixing of Fisherian (p-values) and Neyman–Pearsonian (α levels) ideas about testing, distilled in the customary but misleading p < α criterion of statistical significance, has led researchers in the social and management sciences (and elsewhere) to commonly misinterpret the p-value as a ‘data-adjusted’ Type I error rate. Evidence substantiating this claim is provided from a number of fronts, including comments by statisticians, articles judging the value of significance testing, textbooks, surveys of scholars, and the statistical reporting behaviours of applied researchers. That many investigators do not know the difference between p’s and α’s indicates much bewilderment over what those most ardently sought research outcomes—statistically significant results—means. Statisticians can play a leading role in clearing this confusion. A good starting point would be to abolish the p < α criterion of statistical significance.     

留学生汉语离合词习得偏误调查研究——越南留学生的视角

汉语离合词主要有四种结构类型,据此可设计问卷对初级、中级、高级阶段越南留学生开展离合词习得偏误问卷调查,得出各水平阶段学生使用离合词产生的偏误率。问卷调查中越南留学生产生的偏误在不同离合词结构类型及不同水平阶段两方面有不同表现,成因也不尽相同。调查结果对汉语离合词的教学及留学生学习汉语离合词有一定的启示意义。     

Nonnegative piecewise linear histograms *

We introduce a modified version ?_nof the piecewiss linear hisiugrimi uf Beirlant et al. (1998) which is a true probability density, i.e., ?_n[d] 0 and [d]?_n=1. We prove that ?_nestimates the underlying densitv ? strongly consistently in the L₁mmn, derive large deviation inequalities for the t\ error \?_n- f\ and prove that £||/"-/|| tends to zero with the rate n ^-1\3, We also show that the derivative lf'n estimates consistently in ine expected Lx error the derivative/ of sufficiently smooth density and evaluate the rate of convergence n-i/5 for Epf'n -f'% The estimator/" thus enables to approximate/in the Besov space with a guaranteed rate of convergence. Optimization of the smoothing parameter is also studied. The theoretical or experimentally approximated values of the expected errors E\\?_n- f\\ and E||2?'_n-?' are compared with tiie errors aCiiieveu u-y t"e histogram of Beirlant et ah, and other nonparametric methods.     

On construction of exact experimental designs from discrete ones

In the paper we try to evaluate approximately optimum exact designs of required goodness from a known approximately optimum discrete design. In dependence on this discrete design we define some subsets of the set of all considered exact designs.

Then we look for sufficiency conditions, on which elements of these subsets are approximately optimum (exact) designs of the required goodness. Moreover we offer a method to find such elements, if they exist at all. Finally we prove some statements concerning the existence of such elements for two classes of (optimum) criteria.