Results 241 to 250 of about 353,670 (291)

Multiple Model Regression Estimation

IEEE Transactions on Neural Networks, 2005
This paper presents a new learning formulation for multiple model estimation (MME). Under this formulation, training data samples are generated by several (unknown) statistical models. Hence, most existing learning methods (for classification or regression) based on a single model formulation are no longer applicable.
Vladimir, Cherkassky, Yunqian, Ma
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Estimating Mixtures of Regressions

Journal of Computational and Graphical Statistics, 2003
This article shows how Bayesian inference for switching regression models and their generalizations can be achieved by the specification of loss functions which overcome the label switching problem common to all mixture models. We also derive an extension to models where the number of components in the mixture is unknown, based on the birthand-death ...
Hurn, Merrilee, Justel, Ana, Robert, Christian P.   +2 more
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On Estimating Regression

Theory of Probability & Its Applications, 1964
A study is made of certain properties of an approximation to the regression line on the basis of sampling data when the sample size increases unboundedly.
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Estimation of Multivariate Regression

Theory of Probability & Its Applications, 2004
Summary: Let \((X,Y)\) be a random vector whose first component takes values in a measurable space \(({\mathfrak{X}},{\mathfrak{A}},\mu)\) with measure \(\mu\), and let \(Y\) be a real-valued random variable. Let \(f(x)={\mathbf E}\{Y\mid X=x\} \) be the regression function of \(Y\) on \(X\).
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Regression Analysis and Estimating Regression Models

2020
A forecast is merely a prediction about the future values of data. Financial forecasts span a broad range of areas, and each of the forecasts is of interest to a number of people and departments in a firm. A sales manager may wish to forecast sales (either in units sold or revenues generated).
John B. Guerard, Anureet Saxena, Mustafa N. Gültekin   +2 more
openaire   +1 more source

Estimation of Partial Regression Coefficient

Biometrical Journal, 1993
AbstractA sampling scheme providing unbiased partial regression coefficient has been proposed. The proposed sampling scheme is not only unbiased but also superior to simple random sampling and that due to Singh and Bathla (1990) for estimation of partial regression coefficient.
Singh, Padam, Talwar, Harsh Kumari
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Ridge Estimators in Logistic Regression

Applied Statistics, 1992
Summary: In this paper it is shown how ridge estimators can be used in logistic regression to improve the parameter estimates and to diminish the error made by further predictions. Different ways to choose the unknown ridge parameter are discussed. The main attention focuses on ridge parameters obtained by cross-validation.
le Cessie, S., van Houwelingen, J. C.
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Estimation in Functional Lagged Regression

Journal of Time Series Analysis, 2015
The paper introduces a functional time series (lagged) regression model. The impulse‐response coefficients in such a model are operators acting on a separable Hilbert space, which is the function space L2 in applications. A spectral approach to the estimation of these coefficients is proposed and asymptotically justified under a general nonparametric ...
Hörmann, Siegfried, Kidzinski, Lukasz, Kokoszka, Piotr   +2 more
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Estimating Equivalence with Quantile Regression

Ecological Applications, 2010
Equivalence testing and corresponding confidence interval estimates are used to provide more enlightened statistical statements about parameter estimates by relating them to intervals of effect sizes deemed to be of scientific or practical importance rather than just to an effect size of zero.
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Nonparametric Regression Estimation

2002
The basic aim of mathematical statistics is to learn a probability law or its characteristics from data.
L. Györfi, M. Kohler
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