Conditional mode estimation for functional stationary ergodic data with responses missing at random

In this paper, we investigate the asymptotic properties of a non-parametric conditional mode estimation given a functional explanatory variable, when functional stationary ergodic data and missing at random responses are observed. First of all, we establish asymptotic properties for a conditional density estimator from which we derive almost sure convergence (with rate) and asymptotic normality of a conditional mode estimator. This new estimate take into account missing data, and a simulation study is performed to illustrate how this fact allows to get higher predictive performances than those obtained with standard estimates.     

The effect of parameter estimation on the performance of one-sided Shewhart control charts for zero-inflated processes

ABSTRACT

Zero-inflated probability models are used to model count data that have an excessive number of zeros. Shewhart-type control charts have been proposed for the monitoring of zero-inflated processes. Usually their performance is evaluated under the assumption of known process parameters. However, in practice, their values are rarely known and they have to be estimated from an in-control historical Phase I sample. In the present paper, we investigate the performance of Shewhart-type control charts for zero-inflated processes with estimated parameters and propose practical guidelines for the statistical design of the examined charts, when the size of the preliminary sample is predetermined.     

Optimal estimators for the importance sampling method

The Monte Carlo method gives some estimators to evaluate the expectation [ILM0001] based on samples from either the true density f or from some instrumental density. In this paper, we show that the Riemann estimators introduced by Philippe (1997) can be improved by using the importance sampling method. This approach produces a class of Monte Carlo estimators such that the variance is of order O(n ^?2). The choice of an optimal estimator among this class is discussed. Some simulations illustrate the improvement brought by this method. Moreover, we give a criterion to assess the convergence of our optimal estimator to the integral of interest.     

Nonparametric modelling for functional data: selected survey and tracks for future

Nonparametric functional data analysis is a field whose development started some 15 years ago and there is a very extensive literature on the topic (hundreds of papers published now). The first aim of this survey is to discuss the state of art in the field through a necessarily selected, bibliographical survey. The second aim is to present a wide scope of open questions in order to promote further discussions. Our main purpose is restricted to methodological contributions and also to emphasize on kernel functional regression analysis before extending discussions in two directions: alternative techniques to kernel methods for functional regression and other statistical problems besides regression.     

Bivariate censored regression relying on a new estimator of the joint distribution function

In this paper we study a class of M  -estimators in a regression model under bivariate random censoring and provide a set of sufficient conditions that ensure asymptotic n^1/2-convergence$n^{1/2}\mathrm{-}\mathrm{convergence}$. The cornerstone of our approach is a new estimator of the joint distribution function of the censored lifetimes. A copula approach is used to modelize the dependence structure between the bivariate censoring times. The resulting estimators present the advantage of being easily computable. A simulation study enlighten the finite sample behavior of this technique.     

The pains of immigrant imprisonment

The immigrant detention system in the United States is civil, rather than criminal, and therefore nonpunitive. However, in practice, detained immigrants lacking many basic constitutional protections find themselves in facilities that are often indistinguishable from prisons and jails. In this paper, we explore the crisis of immigrant imprisonment at the affective level, focusing on the painful experiences of immigrant detainees, while also emphasizing its systemic and racialized nature. Specifically, we place a review of a growing body of research that draws connections between immigrant detention and mass imprisonment alongside the findings from numerous reports issued by human rights organizations on the conditions of confinement within immigrant detention facilities. Using a “pains of imprisonment” framework, we highlight four particularly prominent “pains”: containment, exploitation, coercion, and legal violence. We suggest the infliction of such pain, especially when contextualized within a broader history of Latina/o oppression, demonstrates that immigration prisons are in fact punitive, “lawless spaces” where penal oppression is exercised. We conclude with a call for sociologists to become more attentive to this crisis, and to appreciate the similarities between immigration detention and other forms of racialized social control—namely, mass incarceration.