high-dimensional regression problems
Confidence Intervals and Hypothesis Testing for High-Dimensional Regression
... The use of non-linear parameter estimators comes at a price. In general, it is impossible to characterize the distribution of the estimator. This situation is very different from the one of classical statistics in which ...
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High Dimensional Regression on Serum Analytes
... in regression analysis for dimension reduction is the stepwise ...a regression may have a high degree of ...a regression equation may change with a slight change in the data and, thus, are not ...
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High-Dimensional Structured Regression Using Convex Optimization
... I can’t thank enough my advisor Jacob Bien. He is always patient with me, gen- erous with his time, and he is such a wonderful and fun friend to be with. The past five years working with him was nothing less than my best ...
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SUPPORT UNION RECOVERY IN HIGH-DIMENSIONAL MULTIVARIATE REGRESSION 1
... Lasso problems and constructing the union of individually estimated ...our high-dimensional analysis the noise variance does not grow as the dimensionality grows, and thus asymptotically the noise is ...
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Building efficient fuzzy regression trees for large scale and high dimensional problems
... world problem, the Protein Structure Prediction (PSP), which aims to predict the 3D structure of a protein (output variable) based on amino-acid structural continuous variables (inputs). As described in [1], a protein ...
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Joint and Post-Selection Confidence Sets for High-Dimensional Regression
... Another direction is to incorporate the confidence set C b with the method of estimator augmentation (Zhou, 2014; Zhou and Min, 2017) for lasso-based inference. Estimator aug- mentation can be used to simulate from the ...
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Multiple influential point detection in high dimensional regression spaces
... for high dimensional data based on ...for high dimensional ...fixed dimensional problems, there is a general lack of principled procedures for declaring significance for any defined ...
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High-dimensional instrumental variables regression and confidence sets
... We use hierarchies of convex relaxations for the optimization problems in (3.3). These allow for the sparsity constraint, and can be applied when ϕ(b) is a rational function (i.e., a ratio of polynomials) and B ...
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Some Contributions to High Dimensional Mixed Effects Logistic Regression Models
... For methodology contribution, we have first proposed and analyzed the stochas- tic proximal gradient algorithm, which applies the Polya-Gamma MCMC sampler to approximate the loss gradient. In addition, we have developed ...
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High-dimensional support union recovery in multivariate regression
... the regression problems. Study- ing this problem under high-dimensional scaling (where the problem parame- ters as well as sample size n tend to infinity simultaneously), our main result is to ...
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Novel Regression Models For High-Dimensional Survival Analysis
... Figure 3.4 provides the histogram plots of the AUC values for each dataset at four different time points corresponding to 25%, 50%, 75%, and 100% of events in each dataset. To demonstrate the time-dependent prediction ...
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Regularized and robust regression methods for high dimensional data
... multicollinearity problems among the predictors are present or when , the matrix and the matrix ( do not have full ...but high variances and better prediction accuracy can usually be obtained by lowering ...
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Efficient Inferential Methods in Regression Models with Change Points or High Dimensional Covariates.
... non-standard regression set-ups. These problems find application in various disciplines of science including medicine, biology, genomics, environmental sciences and ...for high dimensional ...
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Selected Problems for High-Dimensional Data - Quantile and Errors-in-Variables Regressions.
... regular regression and have positive components over the all time ...error regression are interpreted as being constant, this is consistent with the simple 1-dimensional neuro- encoding hypothesis, ...
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Pattern Alternating Maximization Algorithm for Missing Data in High-Dimensional Problems
... For high-dimensional data, where the number of variables may greatly exceed sample size, we perform regularization using a Lasso-type ...the regression coefficients used for imputation, permits fast ...
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Analysis of Penalized Regression Methods in a Simple Linear Model on the High-Dimensional Data
... into high-dimensional data that may cause many problems in various research, scientific, medical and engineering fields ...for high-dimensional data the number of variables is greater ...
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HIGH-DIMENSIONAL REGRESSION WITH NOISY AND MISSING DATA: PROVABLE GUARANTEES WITH NONCONVEXITY
... of high-dimensional sparse linear regression—in particular, in the case when the predictors or covariates are noisy, missing, and/or ...optimization problems that may be nonconvex; however, ...
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The flare Package for High Dimensional Linear Regression and Precision Matrix Estimation in R
... column regression scheme, we further extend these regression methods to sparse precision matrix estimation, including: (5) TIGER, which is tuning insensitive (Liu and Wang, 2012); (6) CLIME, which can ...
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Confidence Intervals for High-Dimensional Linear Regression: Minimax Rates and Adaptivity
... linear regression. These problems are connected, but each has its own special features and the be- haviors of the problems are different from each ...
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How to Solve Classification and Regression Problems on High-Dimensional Data with a Supervised Extension of Slow Feature Analysis
... from high-dimensional data, ...low- dimensional set of features that can be post-processed by typical supervised algorithms to generate the final label or class ...on regression, where four ...
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