<p>As an example, the distribution of computed from surrogate series of stride time data of one subject is presented with a mean value of for version A and for version B. The respective mean squared errors are (A) and (B).</p
A surrogate end point is often used to evaluate the effects of treatments or exposures on the true e...
AbstractSharp bounds for the Gini mean difference of an empirical distribution are given
Approximation models (or surrogate models) provide an efficient substitute to expen-sive physical si...
The custom in surrogate-based modeling of complex engineering problems is to fit one or more surroga...
Nonparametric estimation of a quantile qm(X),α of a random variable m(X) is considered, where m : ℝd...
<p>This paper demonstrates the good coverage performance of several confidence interval estimators f...
<p>(a) Quantile probability plots for the correct trials. These compare reaction time distributions ...
<p>We determined the distributions of nearest obstacle distances in a binary surrogate data set and ...
We show that the coverage error of confidence intervals and level error of hypothesis tests for popu...
If the distribution of random variable is uknown, we are not able to figure out the value of theoret...
Appropriate sampling of training points is one of the primary factors affecting the fidelity of surr...
Thesis (Ph.D.)--University of Washington, 2019In this dissertation, we study three problems: oracle ...
In this thesis, we study two independent samples under right censoring. Using a smoothed empirical l...
An introductory section shows the behavior of quantile regressions in datasets with different charac...
A method for estimating quantiles and their confidence intervals based on the paper by Francisco-Ful...
A surrogate end point is often used to evaluate the effects of treatments or exposures on the true e...
AbstractSharp bounds for the Gini mean difference of an empirical distribution are given
Approximation models (or surrogate models) provide an efficient substitute to expen-sive physical si...
The custom in surrogate-based modeling of complex engineering problems is to fit one or more surroga...
Nonparametric estimation of a quantile qm(X),α of a random variable m(X) is considered, where m : ℝd...
<p>This paper demonstrates the good coverage performance of several confidence interval estimators f...
<p>(a) Quantile probability plots for the correct trials. These compare reaction time distributions ...
<p>We determined the distributions of nearest obstacle distances in a binary surrogate data set and ...
We show that the coverage error of confidence intervals and level error of hypothesis tests for popu...
If the distribution of random variable is uknown, we are not able to figure out the value of theoret...
Appropriate sampling of training points is one of the primary factors affecting the fidelity of surr...
Thesis (Ph.D.)--University of Washington, 2019In this dissertation, we study three problems: oracle ...
In this thesis, we study two independent samples under right censoring. Using a smoothed empirical l...
An introductory section shows the behavior of quantile regressions in datasets with different charac...
A method for estimating quantiles and their confidence intervals based on the paper by Francisco-Ful...
A surrogate end point is often used to evaluate the effects of treatments or exposures on the true e...
AbstractSharp bounds for the Gini mean difference of an empirical distribution are given
Approximation models (or surrogate models) provide an efficient substitute to expen-sive physical si...
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