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Statistical Models in Epidemiology.
Journal of the American Statistical Association, 19955. Statistical Models in Epidemiology. By D. Clayton and M. Hills. ISBN 0 19 852221 5. Oxford University Press, Oxford, 1993. 368 pp. £30.
M. K., David Clayton, Michael Hills
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2019
In this chapter, we present statistical modelling approaches for predictive tasks in business and science. Most prominent is the ubiquitous multiple linear regression approach where coefficients are estimated using the ordinary least squares algorithm. There are many derivations and generalizations of that technique. In the form of logistic regression,
Dettling, Marcel, Ruckstuhl, Andreas
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In this chapter, we present statistical modelling approaches for predictive tasks in business and science. Most prominent is the ubiquitous multiple linear regression approach where coefficients are estimated using the ordinary least squares algorithm. There are many derivations and generalizations of that technique. In the form of logistic regression,
Dettling, Marcel, Ruckstuhl, Andreas
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2008
Statistical models provide an alternative approach to using dynamical models in seasonal climate forecasting. In statistical models relationships between one set of data, the predictors, and a second set, the predictands, are sought. Common predictands include seasonal mean temperatures and accumulated precipitation, and are typically predicted using ...
Mason, Simon J., Baddour, Omar
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Statistical models provide an alternative approach to using dynamical models in seasonal climate forecasting. In statistical models relationships between one set of data, the predictors, and a second set, the predictands, are sought. Common predictands include seasonal mean temperatures and accumulated precipitation, and are typically predicted using ...
Mason, Simon J., Baddour, Omar
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2020
This chapter describes the application of statistical concepts with illustration about statistical models, probability, normal distribution, and analysis of variance (ANOVA). Statistical analysis is an important action process in research that deals with data. It follows well-defined, systematic, and mathematical procedures and rules.
Mukhtar Ahmed, Mukhtar Ahmed
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This chapter describes the application of statistical concepts with illustration about statistical models, probability, normal distribution, and analysis of variance (ANOVA). Statistical analysis is an important action process in research that deals with data. It follows well-defined, systematic, and mathematical procedures and rules.
Mukhtar Ahmed, Mukhtar Ahmed
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Statistical Model Choice [PDF]
SSRN Electronic Journal, 2015 Variable selection methods and model selection approaches are valuable statistical tools that are indispensable for almost any statistical modeling question. This review first considers the use of information criteria for model selection. Such criteria provide an ordering of the considered models where the best model is selected.
Andre Davids, Gerda Claeskens
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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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Statistical models and thermalization
Nuclear Physics B - Proceedings Supplements, 2003Abstract The status of thermodynamical is discussed. This approach is quiet popular in the heavy ion collision physics. It is argued that the “principle of vanishing of correlations” must be used for quantitative estimations of the rate of thermalization.
A. Sissakian, J. Manjavidze
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Statistical models and statistical inference
1981In the previous chapter we have seen how a simple statistical model can be fitted to data by estimating the unknown parameters and then making checks with residuals. After we have done this, various questions can be answered in terms of the fitted model.
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Statistical manifolds are statistical models
Journal of Geometry, 2006In this note we prove that any smooth (C1 resp.) statistical manifold can be embedded into the space of probability measures on a finite set. As a result, we get positive answers to Lauritzen’s question and Amari’s question on a realization of smooth (C1 resp.) statistical manifolds as finite dimensional statistical models.
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