A nonparametric test for serial independence of errors in linear regression

A test for serial independence of regression errors, consistent in the direction of first order alternatives, is proposed. The test statistic is a function of a Hoeffding-Blum-Kiefer-Rosenblatt type of empirical process, based on residuals. The resultant statistic converges, surprisingly, to the same limiting distribution as the corresponding statistic based on true errors

Nonparametric and semiparametric estimation with discrete regressors

This paper presents and discusses procedures for estimating regression curves when regressors are discrete and applies them to semiparametric inference problems. We show that pointwise root-n-consistency and global consistency of regression curve estimates are achieved without employing any smoothing, even for discrete regressors with unbounded support. These results still hold when smoothers are used, under much weaker conditions than those required with continuous regressors. Such estimates are useful in semiparametric inference problems. We discuss in detail the partially linear regression model and shape-invariant modelling. We also provide some guidance on estimation in semiparametric models where continuous and discrete regressors are present. The paper also includes a Monte Carlo study

Description of the larval stages of Gymnochthebius jensenhaarupi and phylogenetic analysis of the relationships with other species of the subfamily Ochthebiinae (Coleoptera: Hydraenidae)

The three larval instars of Gymnochthebius jensenhaarupi (Knisch, 1924) are described and illustrated, including a detailed analysis of their chaetotaxy and porotaxy. The specimens used in this study were collected with adults of G. jensenhaarupi and have been identified as such by association. Comparative notes on the morphology of these larvae with other species of the subfamily Ochthebiinae are given. A hypothesis of phylogenetic relationships between G. jensenhaarupi and other members of Ochthebiinae with thoroughly described larvae is presented. The monophyly of Ochthebiinae is supported by additional larval features. On the other hand Ochthebius, as currently composed, seems to by paraphyletic. Gymnochthebius Orchymont, 1943 is confirmed as the sister group of Aulacochthebius Kuwert, 1887.Fil: Delgado, Juan A.. Universidad de Murcia; EspañaFil: Archangelsky, Miguel. Universidad Nacional de la Patagonia "San Juan Bosco"; Argentin

Inference on semiparametric models with discrete regressors

We study statistical properties of coefficient estimates of the partially linear regression model when some or all regressors, in the unknown part of the model, are discrete. The method does not require smoothing in the discrete variables. Unlike when there are continuous regressors. when all regressors are discrete independence between regressors and regression errors is not required. We also give some guidance on how to implement the estimate when there are both continuous and discrete regressors in the unknown part of the model. Weights employed in this paper seem straightforwardly applicable to other semiparametric problems

Nonparametric Tests for Conditional Symmetry in Dynamic Models

This article proposes omnibus tests for conditional symmetry around a parametric function in a dynamic context. Conditional moments may not exist or may depend on the explanatory variables. Test statistics are suitable functionals of the empirical process of residuals and explanatory variables, whose limiting distribution under the null is nonpivotal. The tests are implemented with the assistance of a bootstrap method, which is justified assuming very mild regularity conditions on the specification of the center of symmetry and the underlying serial dependence structure. Finite sample properties are examined by means of a Monte Carlo experiment.Publicad

- A NONPARAMETRIC TEST FOR SERIAL INDEPENDENCE OF REGRESSION ERRORS.

A test for serial independence of regression errors is proposed that is consistent in the direction ofserial dependence alternatives of first order. The test statistic is a function of aHoeffding-Blum-Kiefer-Rosenblatt type of empirical process, based on residuals. The resultantstatistic converges, surprisingly, to the same limiting distribution as the corresponding statisticbased on true errors.Empirical process based on residuals; Hoeffding-Blum-Kiefer-Rosenblatt statistic; Serial independence test

Conditional stochastic dominance testing

This article proposes bootstrap-based stochastic dominance tests for nonparametric conditional distributions and their moments. We exploit the fact that a conditional distribution dominates the other if and only if the difference between the marginal joint distributions is monotonic in the explanatory variable for each value of the dependent variable. The proposed test statistic compares restricted and unrestricted estimators of the difference between the joint distributions, and can be implemented under minimal smoothness requirements on the underlying nonparametric curves and without resorting to smooth estimation. The finite sample properties of the proposed tests are examined by means of a Monte Carlo study. We report an application to studying the impact on post-intervention earnings of the National Supported Work Demonstration, a randomized labor training program carried out in the 1970s.Nonparametric testing, Conditional stochastic dominance, Conditional inequality restrictions, Least concave majorant, Treatment effects

Nonparametric Tests for Conditional Symmetry in Dynamic Models.

This article proposes omnibus tests for conditional symmetry around a parametric function in a dynamic context. Conditional moments may not exist or may depend on the explanatory variables. Test statistics are suitable functionals of the empirical process of residuals and explanatory variables, whose limiting distribution under the null is nonpivotal. The tests are implemented with the assistance of a bootstrap method, which is justified assuming very mild regularity conditions on the specification of the center of symmetry and the underlying serial dependence structure. Finite sample properties are examined by means of a Monte Carlo experiment.Omnibus tests; Symmetry tests; Conditional distributions; Time series; Empirical processes; Bootstrap;

Non-comoving baryons and cold dark matter in cosmic voids

We examine the fully relativistic evolution of cosmic voids constituted by baryons and cold dark matter (CDM), represented by two non-comoving dust sources in a $\Lambda$CDM background. For this purpose, we consider numerical solutions of Einstein's field equations in a fluid-flow representation adapted to spherical symmetry and multiple components. We present a simple example that explores the frame-dependence of the local expansion and the Hubble flow for this mixture of two dusts, revealing that the relative velocity between the sources yields a significantly different evolution in comparison with that of the two sources in a common 4-velocity (which reduces to a Lemaitre-Tolman-Bondi model). In particular, significant modifications arise for the density contrast depth and void size, as well as in the amplitude of the surrounding over-densities. We show that an adequate model of a frame-dependent evolution that incorporates initial conditions from peculiar velocities and large-scale density contrast observations may contribute to understand the discrepancy between the local value of $H_0$ and that inferred from the CMB.Comment: Discussion of the evolution of baryon-CDM relative velocity added. Other minor but important corrections were incorporated. Version accepted for publication in EPJ