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In partially linear model selection, we develop a profiled forward regression (PFR) algorithm for ultrahigh dimensional variable screening. The PFR algorithm effectively combines the ideas of nonparametric profiling and forward regression. This allows us to obtain a uniform bound for the...
Persistent link: https://www.econbiz.de/10013131150
When the functional data are not homogeneous, e.g., there exist multiple classes of functional curves in the dataset, traditional estimation methods may fail. In this paper, we propose a new estimation procedure for the Mixture of Gaussian Processes, to incorporate both functional and...
Persistent link: https://www.econbiz.de/10013072829
We study the efficient estimation of nonparametric regressions with conditional heteroskedasticity in a time series setting. We introduce a weighted local polynomial regression smoother that takes account of the dynamic heteroskedasticity. The effect of weighting on nonparametric regressions is...
Persistent link: https://www.econbiz.de/10013004681
asymptotic properties for the proposed estimators including the uniform consistency with general convergence rates. We further …
Persistent link: https://www.econbiz.de/10012915138
This paper studies the asymptotic normality for the kernel deconvolution estimator when the noise distribution is logarithmic chi-square; both identical and independently distributed observations and strong mixing observations are considered. The dependent case of the result is applied to obtain...
Persistent link: https://www.econbiz.de/10011297541
Permutation techniques, where one recompute the test statistic over permutations of data, have a long history in statistics and have become increasingly useful as the availability of computational power has increased. Until now, no permutation tests for examining returns to scale assumptions,...
Persistent link: https://www.econbiz.de/10013338075
Persistent link: https://www.econbiz.de/10012110387
Persistent link: https://www.econbiz.de/10012166709
We consider two semiparametric models for the weight function in a bias sample model. The object of our interest parametrizes the weight function, and it is either Euclidean or non Euclidean. One of the models discussed in this paper is motivated by the estimation the mixing distribution of...
Persistent link: https://www.econbiz.de/10003633700
Single equation instrumental variable models for discrete outcomes are shown to be set not point identifying for the structural functions that deliver the values of the discrete outcome. Identified sets are derived for a general nonparametric model and sharp set identification is demonstrated....
Persistent link: https://www.econbiz.de/10003785212