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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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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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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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Cointegration and forward and spot exchange rate regressions [PDF]
Journal of International Money and Finance, 2000 Abstract We investigate the relationship between cointegration models of the current spot exchange rate, s t , and the current forward rate, f t , and cointegration models of the future spot rate, s t +1 , and f t and the implications of this relationship for tests of the forward rate unbiasedness hypothesis (FRUH).
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Optimal rate of the regularized regression learning algorithm
International Journal of Computer Mathematics, 2011This paper studies the regularized learning algorithm associated with the least-square loss and reproducing kernel Hilbert space. The target is the error analysis for the regression problem in learning theory. The upper and lower bounds of error are simultaneously estimated, which yield the optimal learning rate. The upper bound depends on the covering
Yongquan Zhang, Feilong Cao, Zongben Xu
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The improved learning rate for regularized regression with RKBSs
International Journal of Machine Learning and Cybernetics, 2016The investigation on the performance of learning from samples of functions in Banach spaces is a new research field. A key theoretical problem we need to investigate is how the convergence rate is influenced by the geometry property of the Banach spaces.
Huanxiang Liu, Baohuai Sheng, Peixin Ye
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A Comparison of Beta Regression and Copula Regression for Partial Lapse Rate Estimate
In actuarial analysis, it is very useful to analyze the behavior of an interval-bounded random variable, as a percentage, a proportion, or a fraction, conditioned to other explanatory variables. For this kind of variables, considering the presence of bounds, in general in (0,1), the estimate of the conditional mean and/or conditional quantiles is more ...Baione, Fabio +2 more
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Adaptive rate control using nonlinear regression
IEEE Transactions on Circuits and Systems for Video Technology, 2003This paper presents a simple, fast, and accurate rate-control algorithm using nonlinear regression that plays a central role in estimation theory. We measure a conditional mean by estimating a joint probability density function (PDF) using Parzen's (1962) window.
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