An empirical likelihood goodness-of-fit test for time series

Summary. Standard goodness-of-fit tests for a parametric regression model against a series of nonparametric alternatives are based on residuals arising from a fitted model. When a parametric regression model is compared with a nonparametric model, goodness-of-fit testing can be naturally approached by evaluating the likelihood of the parametric model within a nonparametric framework. We employ the empirical likelihood for an α -mixing process to formulate a test statistic that measures the goodness of fit of a parametric regression model. The technique is based on a comparison with kernel smoothing estimators. The empirical likelihood formulation of the test has two attractive features. One is its automatic consideration of the variation that is associated with the nonparametric fit due to empirical likelihood's ability to Studentize internally. The other is that the asymptotic distribution of the test statistic is free of unknown parameters, avoiding plug-in estimation. We apply the test to a discretized diffusion model which has recently been considered in financial market analysis.     

Goodness—of—fit for the two—parameter weibull distribution with estimated parameters

Goodness—of—fit statistics based on the empirical distribution function (EDF) are not distribution—free when parameters for the hypothesized distribution are estimated. Tables are percentile values of several EDF statistics are available for the two—parameter Weibull distribution when parameters are estimated by maximum likelihood. To determine how these tabled values change when simpler estimators are employed, percentile scores for EDF goodness—of—fit tests were obtained by Monte—Carlo simulation for maximum likelihood estimators (MLEs), good linear unbiased estimators (GLUEs), and modified Cramer—von Mises, Anderson—Darling, and Watson statistics are presented for GLUEs for both complete and censored samples. Critical values for Kolmogorov—Smirnov statistics were less affected by the method of estimation than were closer for MLEs and MGLUEs than for MGLUEs and GLUEs. On the other hand, MGLUE and GLUE results were much more similar to each other than to the MLE results when censoring was light and sample sizes were large.     

托马斯·J·沃森危机管理策略综观

IBM创始人托马斯·J·沃森是经验主义管理学派的代表人物之一。他紧跟技术革命的步伐,以其远见、意志和决心,有效地把握了销售和市场,创造并满足顾客的需求;他勇于承担社会责任,致力于世界和平;他注重培训,能摆正劳资双方的关系,并积极探索危机管理策略,对企业文化作出了突出的贡献。同时,他又成功地解决了接班人问题,被誉为"世界上最伟大的推销员"。     

Lattice path combinatorics for multiple product identities

Lattice paths are enumerated as walks on a lattice under the Delannoy criterion of vertical, horizontal and upward diagonal steps. Delannoy recursions are generalized for arbitrary weights in each of these directions, where the row elements of the Delannoy triangles correspond to lattice paths for all walks at a given length.     

Paradoxes of American Individualism1

I point to contradictions in American individualism not unlike those suggested by Robin M. Williams Jr. I go on to suggest how twenty‐first‐century sociologists might better understand this aspect of American exceptionalism: not as an egoistic, asocial individualism, but as a covenantal, social voluntarism.     

Do females necessarily have the same occupational status scores as males? A conceptual and empirical examination of the Duncan Socioeconomic Status Index and Nam-Powers Occupational Status Scores

Social Science researchers have advanced important yet somewhat contradictory conclusions regarding the different economic and occupational reward structures faced by men and women. Income and wage differences between men and women have been shown to be sizable and persistent throughout the occupational hierarchy. Conversely, gender differences in occupational status, commonly scaled by the Duncan Socioeconomic Index, have been shown to be small or nonexistent in most studies. In an attempt to investigate this incongruity, the present study undertakes a comparison of the Duncan SEI and the Nam-Powers Occupational Status Scores in an empirical study of the occupational position of white men and women in 65 large standard metropolitan statistical areas (SMSAs) in 1970. While the findings of earlier studies showing no gender differences in occupational status are for the most part replicated using the Duncan SEI, use of the Nam-Powers scores prompts a much different conclusion. Large status differences between men and women are indicated using this latter scale, differences which are very much in line with income differences commonly cited. We suggest that the Nam-Powers metric should be used instead of the Duncan SEI in studies of occupational status of women and men.     

Smoothing for discrete-valued time series

We deal with smoothed estimators for conditional probability functions of discrete-valued time series { Y_t } under two different settings. When the conditional distribution of Y_t given its lagged values falls in a parametric family and depends on exogenous random variables, a smoothed maximum (partial) likelihood estimator for the unknown parameter is proposed. While there is no prior information on the distribution, various nonparametric estimation methods have been compared and the adjusted Nadaraya–Watson estimator stands out as it shares the advantages of both Nadaraya–Watson and local linear regression estimators. The asymptotic normality of the estimators proposed has been established in the manner of sparse asymptotics, which shows that the smoothed methods proposed outperform their conventional, unsmoothed, parametric counterparts under very mild conditions. Simulation results lend further support to this assertion. Finally, the new method is illustrated via a real data set concerning the relationship between the number of daily hospital admissions and the levels of pollutants in Hong Kong in 1994–1995. An ad hoc model selection procedure based on a local Akaike information criterion is proposed to select the significant pollutant indices.     

ROBUST COMPARISON OF DISPERSIONS FOR SAMPLES OF DIRECTIONAL DATA

Robust methods are proposed for testing whether several directional distributions on the unit p-sphere have comparable dispersions. The families of distributions considered are the Langevin for random vectors, and the Generalised Scheidegger-Watson for random axes, with specific interest in the Fisher and Watson distributions on the sphere. The methods are analogues of Levene's procedure for comparing variances of normal distributions.     

Hypothesis Tests for Covariance Analysis Models for Circular Data

We introduce covariance analysis models for circular dependent variables. In addition, we have also developed hypothesis tests to evaluate the significance of the parameters used. One of the tests is an extension of the Watson–Williams test. The tests performances have been evaluated using simulation studies.     

The analysis of some biological data

This paper considers the building of stochastic models and the related analysis of discrete data in two biological problems, The first arises from the reproduction of yeast cells, while the secondis concerned with the aggregation of nucleoli. Galton-Watson and aggregation models are constructed for the respective processes and their goodness of fit to the data tested