Toward Comprehensive Refugee Legislation in Hong Kong? Reflections on Reform of the 'Torture Screening' Procedures

Commentpublished_or_final_versio

Convergence rates for trigonometric and polynomial-trigonometric regression estimators

Upper bounds are derived for the rates of convergence for trigonometric series regression estimators of an unknown, smooth regression function. The resulting rates depend on the regression function satisfying certain periodic boundary conditions that may not hold in practice. To overcome such difficulties alternative estimators are proposed which are obtained by regression on trigonometric functions and low-order polynomials. These estimators are shown to always be capable of obtaining the optimal rates of convergence over a particular smoothness class of functions, irregardless of whether or not the regression function is periodic.Guaranteed rates mean squared error nonparametric regression orthogonal series

D.: Existence of the mle and propriety of posteriors for a general multinomial choice model. Statistica Sinica 19

Abstract: This paper examines necessary and sufficient conditions for the existence of Maximum Likelihood Estimates (MLE) and the propriety of the posterior under a bounded improper prior density for a wide class of discrete (or multinomial) choice models. The choice models are based on the principle of utility maximization. Our results cover a wide class of latent variable distributions defining the utility, including in particular multinomial logistic and probit classification and choice models as special cases. Albert and Anderson (1984) gave separation and overlap conditions for the existence of the MLE in logistic classification models. We generalize their conditions to multinomial choice models, giving necessary and sufficient conditions for the existence of a finite MLE and the propriety of the posterior for a wide class of bounded improper priors. Consistency and asymptotic normality for both the MLE and the posterior are also proved under mild conditions

Multivariate regression trees for analysis

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