Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 19 of 19 for “"Penalized Likelihood"”.
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Statistical Methods For Biomarker Threshold Models in Clinical Trials
… often unknown. For this situation, the ordinary likelihood ratio test cannot be applied for testing treatment-biomarker interaction because of the model irregularities. We develop a residual bootstrap method to approximate the distribution of a proposed test statistic to test for …
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Variable screening and graphical modeling for ultra-high dimensional longitudinal data
… longitudinal data. In chapter 3, we propose a penalized likelihood approach to identify the edges in a conditional independence graph for longitudinal data. We used pairwise coordinate descent combined with second order cone programming to optimize the penalized likelihood and estimate the …
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On estimating the score function and the choice of the smoothing parameter with applications to adaptive estimations
… not only because it is based directly on penalized likelihood methods for the score function rather than some other related quantity.
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Methods for the estimation and application of biological networks
… cancer and healthy cells. We describe a convex penalized likelihood equation whose solution has desirable properties for joint network estimation, and we detail an algorithm for its solution. The second method is a test for biologically meaningful changes in the pattern of co-regulation in …
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Statistical Modeling to Information Retrieval for Searching from Big Text Data and Higher Order Inference for Reliability
… where X and Y are independently distributed. A penalized likelihood method is proposed to handle the numerical complications of maximizing the constrained likelihood model. Simulation studies are conducted on two distributions: Burr type X distribution and exponentiated exponential distribution. …
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Flexible Modellierung kategorialer Responsevariablen
… models. Estimates are obtained by maximizing a penalized likelihood with discrete penalty terms restricting the variation of estimated smooth effects. As a result of theoretical considerations, P-Splines seem to be the ideal alternative for applying penalized basis function approaches. Based on …
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Local Likelihood for Interval-censored and Aggregated Point Process Data
The use of the local likelihood method (Tibshirani and Hastie, 1987; Loader, 1996) in the presence of interval-censored or aggregated data leads to a natural consideration of an EM-type strategy, or rather a local EM algorithm. In the thesis, we consider local EM to analyze the point process data …
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Multivariate nonparametric estimation on censored panel data
… a nonparametric mass point approach to marginal likelihood estimation is possible due to Lindsay's characterization of the mixture density. However, their method makes the strong assumption that observed variables are uncorrelated with unobserved heterogeneity and that a small number of mass …
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Improving Mixture Cure Modelling of Multiple Molecular Factors in Cancer Prognosis
… In samples with few events, standard maximum likelihood (ML) estimates can be biased and Wald-type confidence intervals may not be valid. This problem can be exacerbated when multiple imputation is used to deal with missing covariate values. Motivated by a cohort study of breast cancer …
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Fast algorithms for Bayesian variable selection
… There are generally two approaches, one based on penalized likelihood, and the other based on Bayesian framework. We focus on the Bayesian framework in which a hierarchical prior is imposed on all unknown parameters including the unknown variable set. The Bayesian approach has many advantages, for …
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Model-based methods for high-dimensional multivariate analysis
… main parts. In the first part, we propose a penalized likelihood method to fit the linear discriminant analysis model when the predictor is matrix valued. We simultaneously estimate the means and the precision matrix, which we assume has a Kronecker product decomposition. Our penalties …
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Semiparametric analysis of multivariate longitudinal data
… problem, we adopt the idea behind the nonconcave penalized likelihood approach proposed in Fan and Li (2001) and develop a nonconcave penalized estimating function approach. The proposed approach selects variables and estimates regression coefficients simultaneously and an algorithm is presented …
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Statistical Methods for Genetic Pathway-Based Data Analysis
… is for gene network. We develop a multilevel L1 penalized likelihood approach to achieve the sparseness on both levels. We also provide an iterative weighted graphical LASSO algorithm (Guo et al., 2011) for MGGM. Some asymptotic properties of the estimator are also illustrated. Our simulation …
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Statistical methods for modeling RNA-Seq short-read data
… calculate RNA expression, the first using a penalized regression approach to remove bias based on nucleotide composition, as well as a second which demonstrates the use of variation as an estimate of gene expression. Another method is developed which utilizes RNA-Seq gene expression data to …
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C-ARM TOMOGRAPHIC IMAGING TECHNIQUE FOR DETECTION OF KIDNEY STONES
… reconstruction technique (SART), maximum likelihood expectation maximization (MLEM), ordered- subset maximum likelihood expectation maximization (OS-MLEM) and Pre-computed penalized likelihood reconstruction (PPL). Three reconstruction methods were investigated including: pixel-driven …
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Model Selection with Information Criteria
This thesis is on model selection using information criteria. The information criteria include generalized information criterion and a family of Bayesian information criteria. The properties and improvement of the information criteria are investigated. We analyze nonasymptotic and asymptotic …
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Cure Rate Models with Nonparametric Form of Covariate Effects
This thesis focuses on development of spline-based hazard estimation models for cure rate data. Such data can be found in survival studies with long term survivors. Consequently, the population consists of the susceptible and non-susceptible sub-populations with the latter termed as "cured". The …
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Development and implementation of efficient noise suppression methods for emission computed tomography
… components, and then preferentially penalized the high spatial-frequency components. The DCT-induced framelet transform of the natural radiotracer distribution image is sparse. By using this property, we were able to effectively suppress image noise without overly compromising spatial …
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Time-Varying Coefficient Models for Recurrent Events
… the first part, I propose an approach based on penalized B-splines to obtain smooth estimation for both time-varying coefficients and the log baseline intensity. An EM algorithm is developed for parameter estimation. One issue with this approach is that the estimating procedure is conditional on …