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Showing 1 to 20 of 14193 for “"Regression"”.
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Competitive regression.
… the predictive complexity of online regression. In essence, supervised learning from a data sequence consisting of n-dimensional input and the corresponding output is considered. In this work online learning scenario considered consists of sequential arrival of data, without making …
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Some topics on robust nonparametric regression and regression quantiles
… estimates are considered in the nonparametric regression and the generalized linear models. The consistency and asymptotic normality of kernel estimates are proved. Simulations on B-spline estimates for nonparametric regression and generalized linear models are provided.
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Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning
… reduction framework, called {\bf Spectral Regression} (SR). SR casts the problem of learning an embedding function into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression as a building block, different kinds of regularizers can be naturally …
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Binary Regression Quantiles
Binary regression quantiles are solutions to difficult optimization problems that requires the use of a global optimizer. Chapter 5 reviews some of the literature on simulated annealing, a method widely used in a diverse array of fields to solve demanding optimization problems.
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Fair Selective Regression
Selective regression allows for abstention from prediction when uncertainty is high, creating a tradeoff between coverage rate and prediction error. In this thesis, we consider how selective regression interacts with data that is partitioned into subgroups by a sensitive attribute. Specifically, we …
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CROSS VALIDATION METHOD ON WEIGHTED ISOTONIC REGRESSION FOR NONPARAMETRIC REGRESSION FITTING
This thesis discusses the local polynomial regression and the isotonic regression method to solve the nonparametric regression problem subject to the non-decreasing condition. To solve the continuous isotonic regression problem, Pool Adjacent Violators Algorithm (PAVA) is used by updating the local …
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Blind regression : nonparametric regression for latent variable models via collaborative filtering
… have become ubiquitous. We introduce blind regression, a framework motivated by matrix completion for recommender systems: given m users, n items, and a subset of user-item ratings, the goal is to predict the unobserved ratings given the data, i.e., to complete the partially observed matrix. …
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Smooth regression quantile estimation
… be mainly focused on the local linear kernel regression quantile estimation. Different estimators within this class have been proposed, developed asymptotically and applied to real applications. I include algorithmdesign and selection of smoothing parameters. Chapter 2 studies two estimators, …
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The Isotonic Regression Framework
… of the dose-response relationship. Isotonic regression is an alternative proposal when one wishes to establish a dose-response relationship, categorize continuous variables and estimate threshold values. The only assumption for this approach is the monotonicity in the response variable. The …
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The Critique of Regression
… wishes capable of animating a dream. The idea of regression, with all of its intrigue, would shape a century of theory building. It would also become one of the thorniest, if recently neglected, areas of inquiry. The history of the concept attests to two interwoven but distinct traditions. One …
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Smooth Shape-restricted Regression
… and reduce overfitting if the underlying regression function takes the specific form. The classic least squares solutions for shape-restricted estimation are typically neither smooth nor parsimonious. There are many researches investigating the smooth shape-restricted regressors in recent …
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Bayesian Inference in Regression
Made available in DSpace on 2014-12-13T18:21:45Z (GMT). No. of bitstreams: 1 7709067.pdf: 7574042 bytes, checksum: 92b2c4bc545c6d4ae67a5fa75e6ee06f (MD5) Previous issue date: 1976
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Bayesian quantile linear regression
Quantile regression, as a supplement to the mean regression, is often used when a comprehensive relationship between the response variable and the explanatory variables is desired. The traditional frequentists’ approach to quantile regression was well developed with asymptotic theories and …
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Dual Model Robust Regression
In typical normal theory regression, the assumption of homogeneity of variances is often not appropriate. Instead of treating the variances as a nuisance and transforming away the heterogeneity, the structure of the variances may be of interest and it is desirable to model the variances. Aitkin …
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Model-robust quantal regression
… commonly used parametric procedure is logistic regression, commonly referred to as "logit analysis." The adequacy of the fit by the logistic regression curve is tested using the chi-square lack-of-fit test. If the lack-of-fit test is not significant, then the logistic model is assumed to be …
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Modified principal components regression
… variances of the least squares estimators of the regression coefficients become very large. The least squares estimator of the vector of the regression coefficients, which can be written in terms of latent roots and latent vectors of X'X, tends to place heavy weights on the latent vectors …
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Robust mixtures of regression models
… project, we propose a new robust mixture of regression models (Bai et al., 2012). The existing methods for tting mixture regression models assume a normal distribution for error and then estimate the regression param- eters by the maximum likelihood estimate (MLE). In this project, we …
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Incremental Learning in Regression Contexts
… or specific objectives like Personalization. Regression is a procedure, that learns the relationship between independent input variables and dependent, continuous, output variables. In that, it stands in contrast to Classification, where the dependent output variable is discrete. While …
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