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Forecasting exchange rates: A robust regression approach [PDF]

International Journal of Forecasting, 2005
The least squares estimation method as well as other ordinary estimation method for regression models can be severely affected by a small number of outliers, thus providing poor out-of-sample forecasts. This paper suggests a robust regression approach, based on the S-estimation method, to construct forecasting models that are less sensitive to data ...
PREMINGER, Arie, FRANCK, Raphael
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Learning rate of distribution regression with dependent samples

Journal of Complexity, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shunan Dong, Wenchang Sun
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Explaining Unemployment Rates with Symbolic Regression

2014
Much of the research on the accuracy of symbolic regression (SR) has focused on artificially constructed search problems where there is zero noise in the data. Such problems admit of exact solutions but cannot tell us how accurate the search process is in a noisy real world domain. To explore this question symbolic regression is applied here to an area
Philip Truscott, Michael F. Korns
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LEARNING RATES OF REGULARIZED REGRESSION FOR FUNCTIONAL DATA

International Journal of Wavelets, Multiresolution and Information Processing, 2009
The 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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On the regression analysis of tumour recurrence rates

Statistics in Medicine, 1989
AbstractRegression models with mixture (random) components are proposed for the statistical analysis of recurrent events when waiting times between successive events are unknown. These models allow adjustment of parameter estimates for unobserved heterogeneity in the population (due for example to missing covariates) or overdispersion resulting from ...
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A regression method for modelling geometric rates

Statistical Methods in Medical Research, 2015
The occurrence of an event of interest over time is often summarized by the incidence rate, defined as the average number of events per person-time. This type of rate applies to events that may occur repeatedly over time on any given subject, such as infections, and Poisson regression represents a natural regression method for modelling the effect of ...
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Rates and Poisson regression

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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The Analysis of Rates Using Poisson Regression Models

Biometrics, 1983
Models 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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Rate of convergence of the density estimation of regression residual

Statistics & Risk Modeling, 2013
Abstract 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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On Convergence Rates of Convex Regression in Multiple Dimensions

INFORMS Journal on Computing, 2014
We 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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