Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Institution
- Keyword
-
- SPSS (4)
- Correlation (3)
- Missing data (3)
- Multiple imputation (3)
- Nonparametric (3)
-
- Sample size (3)
- Syntax (3)
- Auxiliary variable (2)
- Bootstrap (2)
- Confidence interval (2)
- Efficiency (2)
- Estimation uncertainty (2)
- Expectation-Maximization (2)
- Heteroscedasticity (2)
- Incomplete data (2)
- Lindley distribution (2)
- Monte Carlo (2)
- Nonparametric regression (2)
- Nonresponse (2)
- Outlier (2)
- R (2)
- Ratio imputation (2)
- Regression (2)
- SAS (2)
- Simulation (2)
- Statistics (2)
- Symmetry (2)
- T-test (2)
- Variance (2)
- ANCOVA (1)
- Publication
- Publication Type
Articles 91 - 93 of 93
Full-Text Articles in Statistical Theory
Nonparametric Compound Estimation, Derivative Estimation, And Change Point Detection, Sisheng Liu
Nonparametric Compound Estimation, Derivative Estimation, And Change Point Detection, Sisheng Liu
Theses and Dissertations--Statistics
Firstly, we reviewed some popular nonparameteric regression methods during the past several decades. Then we extended the compound estimation (Charnigo and Srinivasan [2011]) to adapt random design points and heteroskedasticity and proposed a modified Cp criteria for tuning parameter selection. Moreover, we developed a DCp criteria for tuning paramter selection problem in general nonparametric derivative estimation. This extends GCp criteria in Charnigo, Hall and Srinivasan [2011] with random design points and heteroskedasticity. Next, we proposed a change point detection method via compound estimation for both fixed design and random design case, the adaptation of heteroskedasticity was considered for the method. …
Informational Index And Its Applications In High Dimensional Data, Qingcong Yuan
Informational Index And Its Applications In High Dimensional Data, Qingcong Yuan
Theses and Dissertations--Statistics
We introduce a new class of measures for testing independence between two random vectors, which uses expected difference of conditional and marginal characteristic functions. By choosing a particular weight function in the class, we propose a new index for measuring independence and study its property. Two empirical versions are developed, their properties, asymptotics, connection with existing measures and applications are discussed. Implementation and Monte Carlo results are also presented.
We propose a two-stage sufficient variable selections method based on the new index to deal with large p small n data. The method does not require model specification and especially focuses …
A New Approximation Scheme For Monte Carlo Applications, Bo Jones
A New Approximation Scheme For Monte Carlo Applications, Bo Jones
CMC Senior Theses
Approximation algorithms employing Monte Carlo methods, across application domains, often require as a subroutine the estimation of the mean of a random variable with support on [0,1]. One wishes to estimate this mean to within a user-specified error, using as few samples from the simulated distribution as possible. In the case that the mean being estimated is small, one is then interested in controlling the relative error of the estimate. We introduce a new (epsilon, delta) relative error approximation scheme for [0,1] random variables and provide a comparison of this algorithm's performance to that of an existing approximation scheme, both …