Results 261 to 270 of about 3,930,451 (296)
Cointegration and forward and spot exchange rate regressions [PDF]
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).
openaire +2 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
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
openaire +1 more source
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
openaire +1 more source
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.
openaire +1 more source
Analysis of fractional turnover rates by exponential regression
Computer Methods and Programs in Biomedicine, 1992A recent article (Leonhardt et al. Comput. Methods Prog. Biomed. 32 (1990) 345-350) described the analysis of biological turnover data using a model involving an exponential decay from an initial value to a plateau. The authors included a BASIC program for this analysis, claiming that no comparable programs have been published.
openaire +4 more sources
Convergence rates for constrained regression splines
Journal of Statistical Planning and Inference, 2018Abstract Convergence rates of regression spline estimators have been established for a general framework in statistical modeling. It is well known that q th-order regression splines have optimal rates under mild assumptions. Increasing the number of knots tends to improve the approximation error rate but worsen the estimation error rate, and the
Mary C. Meyer +2 more
openaire +1 more source
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
openaire +2 more sources
Online Regression with Controlled Label Noise Rate
2017Many online regression (and adaptive filtering) algorithms are linear, use additive update and designed for the noise-free setting. We consider the practical setting where the algorithm’s feedback is noisy, rather than a clean label. We propose a new family of algorithms which modifies the learning rate based on the noise-variance of the feedback ...
Edward Moroshko, Koby Crammer
openaire +1 more source
Hazard Rate Regression Using Ordinary Nonparametric Regression Smoothers
Journal of Computational and Graphical Statistics, 1996Abstract This article proposes a method for nonparametric estimation of hazard rates as a function of time and possibly multiple covariates. The method is based on dividing the time axis into intervals, and calculating number of event and follow-up time contributions from the different intervals.
openaire +1 more source

