Identifiable finite mixtures of location models for clustering mixed-mode data

For clustering mixed categorical and continuous data, Lawrence and Krzanowski (1996) proposed a finite mixture model in which component densities conform to the location model. In the graphical models literature the location model is known as the homogeneous Conditional Gaussian model. In this paper it is shown that their model is not identifiable without imposing additional restrictions. Specifically, for g groups and m locations, (g!)m–1 distinct sets of parameter values (not including permutations of the group mixing parameters) produce the same likelihood function. Excessive shrinkage of parameter estimates in a simulation experiment reported by Lawrence and Krzanowski (1996) is shown to be an artifact of the model's non-identifiability. Identifiable finite mixture models can be obtained by imposing restrictions on the conditional means of the continuous variables. These new identified models are assessed in simulation experiments. The conditional mean structure of the continuous variables in the restricted location mixture models is similar to that in the underlying variable mixture models proposed by Everitt (1988), but the restricted location mixture models are more computationally tractable.     

Non-parametric Estimation of the Residual Distribution

Consider a heteroscedastic regression model Y = m ( X ) +σ( X )ε, where the functions m and σ are "smooth", and ε is independent of X . An estimator of the distribution of ε based on non-parametric regression residuals is proposed and its weak convergence is obtained. Applications to prediction intervals and goodness-of-fit tests are discussed.     

Job turnover, wage rates, and marital stability: How are they related?

This study examines the interplay between job stability, wage rates, and marital stability. We use a Dynamic Selection Control model in which young men make sequential choices about work and family and estimate the model using an approach that takes account of self-selection, simultaneity and unobserved heterogeneity. The results quantify how job stability affects wage rates, how both affect marital status, and how marital status affects earnings and job stability. The study reveals robust evidence that job changes lower wages and the likelihood of getting married and remaining married. At the same time, marriage raises wage rates and job stability. To project the sequential effects linking job change, marital status, and earnings, we simulate the impacts of shocks that raise preferences for marriage and that increase education. Feedback effects cause the simulated wage gains from marriage to cumulate over time, indicating that long-run marriage wage premiums exceed conventional short-run estimates.     

The non-null distributions in anoya

Under the traditional assumptions, any entry in ANOVA interpreted to include all Linear model analyses] is equivalent in disiributien to a quadratic form Q=[μ₁+σ₁Z₁]²+…+ [μ_ν+σ_νZ_ν]²]wherein Z₁..Z_ν are independent standard normal variables. Test statisics in ANOVE are distributed as ratio R of two depenbent such quadretic forms. The non-null distribution of R is a mixture of null distributions; the mixing variable is an easy generalitatlon of the Poisson variable. Fast algorithms yield the power function in both fixed and random effects models in AVOVA to user-specified accuracy.     

Robust estimation for ordinal regression

Ordinal regression is used for modelling an ordinal response variable as a function of some explanatory variables. The classical technique for estimating the unknown parameters of this model is Maximum Likelihood (ML). The lack of robustness of this estimator is formally shown by deriving its breakdown point and its influence function. To robustify the procedure, a weighting step is added to the Maximum Likelihood estimator, yielding an estimator with bounded influence function. We also show that the loss in efficiency due to the weighting step remains limited. A diagnostic plot based on the Weighted Maximum Likelihood estimator allows to detect outliers of different types in a single plot.     

The governance self‐assessment checklist: An instrument for assessing board effectiveness

This article describes the development and validation of the Governance Self‐Assessment Checklist (GSAC). The GSAC was designed to assist boards of directors of nonprofit and public sector organizations to identify strengths and weaknesses in the governance of their organizations, educate board members about the essentials of good governance, and improve their governance practices. The instrument comprises 144 items organized into twelve subscales. The results of the study indicated that the subscales have excellent internal consistency reliability, exhibit good criterion‐related validity, and are able to discriminate between stronger and weaker aspects of board functioning. The relative strengths and weaknesses in board effectiveness were identified, and the implications of the findings for the assessment of board effectiveness and field applications of the GSAC were discussed.