Results 281 to 290 of about 5,219,358 (316)
Generalized Linear Models [PDF]
The American Statistician, 1994 In this chapter we shall discuss a class of statistical models that generalize the well-understood normal linear model. A normal or Gaussian model assumes that the response Y is equal to the sum of a linear combination X T β of the d-dimensional predictor X and a Gaussian distributed error term. It is well known that the least-squares estimator \(\hat \
Joseph Hilbe, Berwin A. Turlach
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Statistics & Probability Letters, 1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cardot, H., Ferraty, F., Sarda, Pascal
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Cardot, H., Ferraty, F., Sarda, Pascal
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Generalized Linear Models [PDF]
Journal of the American Statistical Association, 2000 A new program for depression is instituted in the hopes of reducing the number of visits each patient makes to the emergency room in the year following treatment. Predictors include (among many others) treatment (yes/no), race, and drug and alcohol usage indices.
Eric Vittinghoff +3 more
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Journal of the American Statistical Association, 2000
(2000). Linear and Log-Linear Models. Journal of the American Statistical Association: Vol. 95, No. 452, pp. 1290-1293.
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(2000). Linear and Log-Linear Models. Journal of the American Statistical Association: Vol. 95, No. 452, pp. 1290-1293.
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1994
Linear models form the core of classical statistics and are still the basis of much of statistical practice; many modern modelling and analytical techniques build on the methodology developed for linear models.
B. D. Ripley, W. N. Venables
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Linear models form the core of classical statistics and are still the basis of much of statistical practice; many modern modelling and analytical techniques build on the methodology developed for linear models.
B. D. Ripley, W. N. Venables
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2021
By means of the Lagrange method or linear optimisation, the relative extremes (minima or maxima) of a linear (target) function can be determined under restrictive linear constraints.
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By means of the Lagrange method or linear optimisation, the relative extremes (minima or maxima) of a linear (target) function can be determined under restrictive linear constraints.
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2013
Mit Hilfe der Lagrange-Methode oder der Linearen Optimierung lassen sich die relativen Extrema (Minimum oder Maximum) einer linearen (Ziel-) Funktion unter einschrankenden linearen Nebenbedingungen (Restriktionen) ermitteln.
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Mit Hilfe der Lagrange-Methode oder der Linearen Optimierung lassen sich die relativen Extrema (Minimum oder Maximum) einer linearen (Ziel-) Funktion unter einschrankenden linearen Nebenbedingungen (Restriktionen) ermitteln.
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WIREs Computational Statistics, 2011
AbstractThis article describes log‐linear models as special cases of generalized linear models. Specifically, log‐linear models use a logarithmic link function. Log‐linear models are used to examine joint distributions of categorical variables, dependency relations, and association patterns.
Patrick Mair +2 more
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AbstractThis article describes log‐linear models as special cases of generalized linear models. Specifically, log‐linear models use a logarithmic link function. Log‐linear models are used to examine joint distributions of categorical variables, dependency relations, and association patterns.
Patrick Mair +2 more
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Classical and Quantum Gravity, 2005
Summary: We try to carry over, as closely as possible, the well-known results for rotational dragging (Thirring, Brill and Cohen) to dragging due to linearly accelerated masses. To this end, a spherical, charged mass shell is linearly accelerated by a (weak) external, axisymmetric and dipolar charge distribution.
Jörg Frauendiener +2 more
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Summary: We try to carry over, as closely as possible, the well-known results for rotational dragging (Thirring, Brill and Cohen) to dragging due to linearly accelerated masses. To this end, a spherical, charged mass shell is linearly accelerated by a (weak) external, axisymmetric and dipolar charge distribution.
Jörg Frauendiener +2 more
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Linear and Non-linear Modeling
2011This chapter describes some of the tools that are available in R for fitting certain kinds of conditional distributions; that is, constructing models to predict the behavior of one random variable given that the value of another one or more is known. Examples of such models in forestry include height-diameter models, diameter-volume models, and so on ...
Jeff D. Hamann, Andrew P. Robinson
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