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- Adaptive Dantzig variable selector; Censored linear regression; Buckley-James imputation; Model selection consistency; Asymptotic normality (1)
- Bayesian Additive Regression Trees (1)
- Bayesian Inference (1)
- Bayesian trees (1)
- Brain tumor (1)
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- Genetics (1)
- Glioblastoma multiforme (1)
- Glioma (1)
- Prediction; estimator selection; model selection; cross-validation; machine learning; model assessment (1)
- Principled sure independence screening; Multiple myeloma; Variable selection; Sure independence screening; Cox model; Ultra-high-dimensional covariates (1)
- Random forest (1)
- Survival analysis (1)
- Tree-ensemble models (1)
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Articles 1 - 6 of 6
Full-Text Articles in Microarrays
Minimum Description Length Measures Of Evidence For Enrichment, Zhenyu Yang, David R. Bickel
Minimum Description Length Measures Of Evidence For Enrichment, Zhenyu Yang, David R. Bickel
COBRA Preprint Series
In order to functionally interpret differentially expressed genes or other discovered features, researchers seek to detect enrichment in the form of overrepresentation of discovered features associated with a biological process. Most enrichment methods treat the p-value as the measure of evidence using a statistical test such as the binomial test, Fisher's exact test or the hypergeometric test. However, the p-value is not interpretable as a measure of evidence apart from adjustments in light of the sample size. As a measure of evidence supporting one hypothesis over the other, the Bayes factor (BF) overcomes this drawback of the p-value but lacks …
Principled Sure Independence Screening For Cox Models With Ultra-High-Dimensional Covariates, Sihai Dave Zhao, Yi Li
Principled Sure Independence Screening For Cox Models With Ultra-High-Dimensional Covariates, Sihai Dave Zhao, Yi Li
Harvard University Biostatistics Working Paper Series
No abstract provided.
The Strength Of Statistical Evidence For Composite Hypotheses: Inference To The Best Explanation, David R. Bickel
The Strength Of Statistical Evidence For Composite Hypotheses: Inference To The Best Explanation, David R. Bickel
COBRA Preprint Series
A general function to quantify the weight of evidence in a sample of data for one hypothesis over another is derived from the law of likelihood and from a statistical formalization of inference to the best explanation. For a fixed parameter of interest, the resulting weight of evidence that favors one composite hypothesis over another is the likelihood ratio using the parameter value consistent with each hypothesis that maximizes the likelihood function over the parameter of interest. Since the weight of evidence is generally only known up to a nuisance parameter, it is approximated by replacing the likelihood function with …
Super Learner In Prediction, Eric C. Polley, Mark J. Van Der Laan
Super Learner In Prediction, Eric C. Polley, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
Super learning is a general loss based learning method that has been proposed and analyzed theoretically in van der Laan et al. (2007). In this article we consider super learning for prediction. The super learner is a prediction method designed to find the optimal combination of a collection of prediction algorithms. The super learner algorithm finds the combination of algorithms minimizing the cross-validated risk. The super learner framework is built on the theory of cross-validation and allows for a general class of prediction algorithms to be considered for the ensemble. Due to the previously established oracle results for the cross-validation …
Survival Prediction For Brain Tumor Patients Using Gene Expression Data, Vinicius Bonato
Survival Prediction For Brain Tumor Patients Using Gene Expression Data, Vinicius Bonato
Dissertations and Theses (Open Access)
Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. …
A New Class Of Dantzig Selectors For Censored Linear Regression Models, Yi Li, Lee Dicker, Sihai Dave Zhao
A New Class Of Dantzig Selectors For Censored Linear Regression Models, Yi Li, Lee Dicker, Sihai Dave Zhao
Harvard University Biostatistics Working Paper Series
No abstract provided.