Asymptotic Properties of Probability Measure Estimators in a Nonparametric Model
نویسندگان
چکیده
منابع مشابه
Asymptotic Properties of Probability Measure Estimators in a Nonparametric Model
We consider probability measure estimation in a nonparametric model using a leastsquares approach under the Prohorov metric framework. We summarize the computational methods and their convergence results that were developed by our group over the past two decades. New results are presented on the bias and the variance due to the approximation and the pointwise asymptotic normality of the approxi...
متن کاملFractional Probability Measure and Its Properties
Based on recent studies by Guy Jumarie [1] which defines probability density of fractional order and fractional moments by using fractional calculus (fractional derivatives and fractional integration), this study expands the concept of probability density of fractional order by defining the fractional probability measure, which leads to a fractional probability theory parallel to the classical ...
متن کاملAsymptotic Properties of Back tting Estimators
When additive models with more than two covariates are tted with the backktting algorithm proposed by Buja et al. 2], the lack of explicit expressions for the estimators makes study of their theoretical properties cumbersome. Recursion provides a convenient way to extend existing theoretical results for bivariate additive models to models of arbitrary dimension. In the case of local polynomial ...
متن کاملLimiting Properties of Empirical Bayes Estimators in a Two-Factor Experiment under Inverse Gaussian Model
The empirical Bayes estimators of treatment effects in a factorial experiment were derived and their asymptotic properties were explored. It was shown that they were asymptotically optimal and the estimator of the scale parameter had a limiting gamma distribution while the estimators of the factor effects had a limiting multivariate normal distribution. A Bootstrap analysis was performed to ill...
متن کاملImproved Rates and Asymptotic Normality for Nonparametric Neural Network Estimators
Barron (1993) obtained a deterministic approximation rate (in L2-norm) of r-l12. for a class of single hidden layer feedforward artificial neural networks (ANN) with r hidden units and sigmoid activation functions when the target function satisfies certain smoothness conditions. Hornik, Stinchcombe, White, and Auer (HSWA, 1994) extended Barron's result to a class of ANNs with possibly non-sigmo...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SIAM/ASA Journal on Uncertainty Quantification
سال: 2015
ISSN: 2166-2525
DOI: 10.1137/140972639