We consider univariate nonparametric regression. Two standard nonparametric regression function estimates are kernel estimates and nearest neighbor estimates. Mack (1981) noted that both methods can be defined with respect to a kernel or weighting function, and that for a given kernel and a...

Motivated by a nonparametric GARCH model we consider nonparametric additive regression and autoregression models in the special case that the additive components are linked parametrically. We show that the parameter can be estimated with parametric rate and give the normal limit. Our procedure...

We consider the partially linear model relating a response Y to predictors (X,T) with mean function XT ß + g (T) when the X's are measured with additive error. The semiparametric likelihood estimate of Severini and Staniswalis (1994) leads to biased estimates of both the parameter ß and the...

Patent litigation is a visible and widespread feature of the semiconductor industry, as firms pursue judicial mechanisms to defend, or promote, their intellectual property portfolios. This study highlights the antecedents, strategic goals, tactics and outcomes of the most significant US trial of...

Consider a regression situation in which one wants to understand whether variability of the response is related to a scalar predictor z; the latter could be the predicted value. A diagnostic for heteroscedasticity is the score test (Cook and Weisberg, 1983), which is equivalent to computing the...

We propose robust and bounded influence methods for linear regression when some of the predictors are measured with error. We address the important special case that the surrogate predictors are replicated, and that the measurement errors in response and predictors are correlated. The robust...