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LEARNING RATES OF REGULARIZED REGRESSION FOR FUNCTIONAL DATA
International Journal of Wavelets, Multiresolution and Information Processing, 2009The study of regularized learning algorithms is a very important issue and functional data analysis extends classical methods. We establish the learning rates of the least square regularized regression algorithm in reproducing kernel Hilbert space for functional data. With the iteration method, we obtain fast learning rate for functional data.
Yong-Li Xu, Di-Rong Chen
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Rate of convergence of the density estimation of regression residual
Statistics & Risk Modeling, 2013Abstract Consider the regression problem with a response variable Y and with a d-dimensional feature vector X. For the regression function m(x) = E{Y|X = x}, this paper investigates methods for estimating the density of the residual Y − m(X) from independent and identically distributed data. If the density is twice differentiable and has
Györfi, László, Walk, Harro
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2008
Epidemiological studies often involve the calculation of rates, typically rates of death or incidence rates of a chronic or acute disease. This is based upon counts of events occurring within a certain amount of time. The Poisson regression method is often employed for the statistical analysis of such data. However, data that are not actually counts of
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Epidemiological studies often involve the calculation of rates, typically rates of death or incidence rates of a chronic or acute disease. This is based upon counts of events occurring within a certain amount of time. The Poisson regression method is often employed for the statistical analysis of such data. However, data that are not actually counts of
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Quantile regression for rating teams
Statistical Modelling, 2007Quantile regression is proposed for modeling game out comes and as the basis for rating teams. The model includes the standard location model for team strength as a special case, while allowing for a richer specification in which teams differ according to the quantiles of the out come distribution.
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The Analysis of Rates Using Poisson Regression Models
Biometrics, 1983Models are considered in which the underlying rate at which events occur can be represented by a regression function that describes the relation between the predictor variables and the unknown parameters. Estimates of the parameters can be obtained by means of iteratively reweighted least squares (IRLS).
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On Convergence Rates of Convex Regression in Multiple Dimensions
INFORMS Journal on Computing, 2014We consider a least squares estimator for estimating a convex function f*: [0, 1]d → ℝ with bounded subgradients. A rate at which the sum of squared differences between the estimator and the true function f* converges to zero is computed. This work sheds light on computing the convergence rate of the multidimensional convex regression estimator.
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Regression Rates Study of Mixed Hybrid Propellants
Journal of Propulsion and Power, 2005The low regression rates of classic hybrid rocket fuels lead to large internal ports that limit potential applications. This experimental study investigated the increase in regression rate that results from adding a solid oxidizer and a catalyst to a hybrid fuel grain.
Robert Frederick +3 more
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The Accuracy of Teachers’ Ratings of Ability: A Regression Model
American Educational Research Journal, 1985Teachers’ rating of academic ability in mathematics and English were regressed on test scores in a sample of second grade students (n = 694 in mathematics, n = 736 in English). Fitted equations were interpreted as judgmental models of the rating process, and normative subjective probabilities were assigned to individual ratings, showing the extent to ...
Owen Egan, Peter Archer
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An Allocation Rule with Wealth‐Regressive Tax Rates
Journal of Public Economic Theory, 2002We introduce a public good allocation rule whose direct implementation by asking agents their endowments leads to Nash equilibrium outcomes—always Pareto dominating voluntary contributions outcomes. Although the Nash equilibrium allocations induced by this rule are not Pareto optimal in general, they are so in two‐person economies.
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Review Rating with Joint Classification and Regression Model
2018Review rating is a sentiment analysis task which aims to predict a recommendation score for a review. Basically, classification and regression models are two major approaches to review rating, and these two approaches have their own characteristics and strength. For instance, the classification model can flexibly utilize distinguished models in machine
Jian Xu +4 more
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