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

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

- 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

Checkpoint proteins come under scrutiny

Details are emerging of the interactions between the kinetochore and various spindle checkpoint proteins that ensure that sister chromatids are equally divided between daughter cells during cell division

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

Strongly coupled large-angle stimulated Raman scattering of short laser pulses in plasma-filled capillaries

Strongly coupled large-angle stimulated Raman scattering (LA SRS) of a short intense laser pulse proceeds in a plane plasma-filled capillary differently than in a plasma with open boundaries. Oblique mirror reflections off capillary walls partly suppress the lateral convection of scattered radiation and increase the growth rate of the instability: the convective gain of the LA SRS falls with an angle much slower than in an unbounded plasma and even for the near-forward SRS can be close to that of the direct backscatter. The long-term evolution of LA SRS in the interior of the capillary is dominated by quasi-one-dimensional leaky modes, whose damping is related to the transmission of electromagnetic waves through capillary walls.Comment: 11 pages, 6 figures; to be submitted to Physics of Plasma