The problem of prediction is revisited with a view towards going beyond the typical nonparametric setting and reaching a fully model-free environment for predictive inference, i.e., point predictors and predictive intervals. A basic principle of model-free prediction is laid out based on the...

In der Neuen Ökonomischen Geografie (NÖG) spielen Preiseffekte im kumulativen Prozess der räumlichen Konzentration eine entscheidende Rolle als Vorwärtskopplung. Der Aufsatz untersucht den potenziellen Beitrag des Helpman-Modells zur Prädiktion des gesamten und nach Gütergruppen...

This article considers estimation of regression function <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$f$$</EquationSource> </InlineEquation> in the fixed design model <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$$Y(x_i)=f(x_i)+ \epsilon (x_i), i=1,\ldots ,n$$</EquationSource> </InlineEquation>, by use of the Gasser and Müller kernel estimator. The point set <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$$\{ x_i\}_{i=1}^{n}\subset [0,1]$$</EquationSource> </InlineEquation> constitutes the sampling design points, and <InlineEquation ID="IEq4"> <EquationSource...</equationsource></inlineequation></equationsource></inlineequation></equationsource></inlineequation></equationsource></inlineequation>

In this paper, we consider the problem of simultaneous variable selection and estimation for varying-coefficient partially linear models in a “small <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$n$$</EquationSource> </InlineEquation>, large <InlineEquation ID="IEq2"> <EquationSource Format="TEX">$$p$$</EquationSource> </InlineEquation>” setting, when the number of coefficients in the linear part diverges with sample size while the number of varying...</equationsource></inlineequation></equationsource></inlineequation>

In multinomial logit models, the identifiability of parameter estimates is typically obtained by side constraints that specify one of the response categories as reference category. When parameters are penalized, shrinkage of estimates should not depend on the reference category. In this paper we...

When a linear model is chosen by searching for the best subset among a set of candidate predictors, a fixed penalty such as that imposed by the Akaike information criterion may penalize model complexity inadequately, leading to biased model selection. We study resampling-based information...

We propose a sampling approach to bandwidth estimation for a nonparametric regression model with continuous and discrete types of regressors and unknown error density. The unknown error density is approximated by a location-mixture of Gaussian densities with means being the individual errors,...

Combining survey, census, and administrative data improves the precision of survey estimates of mean agricultural land values. A components-of-variance model is developed and applied to cropland value data for the Corn Belt Performance of the model compared with other procedures is tested using...