Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Backfitting (1)
- Bayesian Analysis (1)
- Change-point models (1)
- Continuous outcome (1)
- Correlated observations (1)
-
- EM algorithm (1)
- Gene-gene Interaction (1)
- Generalized Conditional Adaptive Lasso (1)
- Hierarchical Model (1)
- High-dimension (1)
- High-dimensional Data (1)
- Kernel regression (1)
- MANOVA (1)
- Mark-Recapture (1)
- Measurement error (1)
- Mixtures-of-regression (1)
- Multifactor Dimensionality Reduction (1)
- Non-parametric (1)
- Photographic Identification (1)
- Poisson regression (1)
- Profile analysis (1)
- Rank transforms (1)
- Repeated measure (1)
- Risk Score (1)
- Semiparametric models (1)
- Variable Screening (1)
- Variable Selection (1)
- Voice rehabilitation (1)
Articles 1 - 6 of 6
Full-Text Articles in Physical Sciences and Mathematics
Multifactor Dimensionality Reduction With P Risk Scores Per Person, Ye Li
Multifactor Dimensionality Reduction With P Risk Scores Per Person, Ye Li
Theses and Dissertations--Statistics
After reviewing Multifactor Dimensionality Reduction(MDR) and its extensions, an approach to obtain P(larger than 1) risk scores is proposed to predict the continuous outcome for each subject. We study the mean square error(MSE) of dimensionality reduced models fitted with sets of 2 risk scores and investigate the MSE for several special cases of the covariance matrix. A methodology is proposed to select a best set of P risk scores when P is specified a priori. Simulation studies based on true models of different dimensions(larger than 3) demonstrate that the selected set of P(larger than 1) risk scores outperforms the single …
High Dimensional Multivariate Inference Under General Conditions, Xiaoli Kong
High Dimensional Multivariate Inference Under General Conditions, Xiaoli Kong
Theses and Dissertations--Statistics
In this dissertation, we investigate four distinct and interrelated problems for high-dimensional inference of mean vectors in multi-groups.
The first problem concerned is the profile analysis of high dimensional repeated measures. We introduce new test statistics and derive its asymptotic distribution under normality for equal as well as unequal covariance cases. Our derivations of the asymptotic distributions mimic that of Central Limit Theorem with some important peculiarities addressed with sufficient rigor. We also derive consistent and unbiased estimators of the asymptotic variances for equal and unequal covariance cases respectively.
The second problem considered is the accurate inference for high-dimensional repeated …
The Family Of Conditional Penalized Methods With Their Application In Sufficient Variable Selection, Jin Xie
The Family Of Conditional Penalized Methods With Their Application In Sufficient Variable Selection, Jin Xie
Theses and Dissertations--Statistics
When scientists know in advance that some features (variables) are important in modeling a data, then these important features should be kept in the model. How can we utilize this prior information to effectively find other important features? This dissertation is to provide a solution, using such prior information. We propose the Conditional Adaptive Lasso (CAL) estimates to exploit this knowledge. By choosing a meaningful conditioning set, namely the prior information, CAL shows better performance in both variable selection and model estimation. We also propose Sufficient Conditional Adaptive Lasso Variable Screening (SCAL-VS) and Conditioning Set Sufficient Conditional Adaptive Lasso Variable …
Estimation In Partially Linear Models With Correlated Observations And Change-Point Models, Liangdong Fan
Estimation In Partially Linear Models With Correlated Observations And Change-Point Models, Liangdong Fan
Theses and Dissertations--Statistics
Methods of estimating parametric and nonparametric components, as well as properties of the corresponding estimators, have been examined in partially linear models by Wahba [1987], Green et al. [1985], Engle et al. [1986], Speckman [1988], Hu et al. [2004], Charnigo et al. [2015] among others. These models are appealing due to their flexibility and wide range of practical applications including the electricity usage study by Engle et al. [1986], gum disease study by Speckman [1988], etc., wherea parametric component explains linear trends and a nonparametric part captures nonlinear relationships.
The compound estimator (Charnigo et al. [2015]) has been used to …
Accounting For Matching Uncertainty In Photographic Identification Studies Of Wild Animals, Amanda R. Ellis
Accounting For Matching Uncertainty In Photographic Identification Studies Of Wild Animals, Amanda R. Ellis
Theses and Dissertations--Statistics
I consider statistical modelling of data gathered by photographic identification in mark-recapture studies and propose a new method that incorporates the inherent uncertainty of photographic identification in the estimation of abundance, survival and recruitment. A hierarchical model is proposed which accepts scores assigned to pairs of photographs by pattern recognition algorithms as data and allows for uncertainty in matching photographs based on these scores. The new models incorporate latent capture histories that are treated as unknown random variables informed by the data, contrasting past models having the capture histories being fixed. The methods properly account for uncertainty in the matching …
Mixtures-Of-Regressions With Measurement Error, Xiaoqiong Fang
Mixtures-Of-Regressions With Measurement Error, Xiaoqiong Fang
Theses and Dissertations--Statistics
Finite Mixture model has been studied for a long time, however, traditional methods assume that the variables are measured without error. Mixtures-of-regression model with measurement error imposes challenges to the statisticians, since both the mixture structure and the existence of measurement error can lead to inconsistent estimate for the regression coefficients. In order to solve the inconsistency, We propose series of methods to estimate the mixture likelihood of the mixtures-of-regressions model when there is measurement error, both in the responses and predictors. Different estimators of the parameters are derived and compared with respect to their relative efficiencies. The simulation results …