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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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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 Modeling for Management
2008Measurement Scales Modeling Continuous Data Modeling Dichotomous Data Modeling Ordered Data Modeling Unordered Data Neural Networks Approximate Algorithms for Management Problems Other Statistical, Mathematical and Co-pattern Modeling ...
Hutcheson, G., Moutinho, L.
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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.
J. Manjavidze, A. Sissakian
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2005
We will outline some of the important problems of extending the relational model for processing statistical data bases. We achieve this by augmenting to relational algebra few more numerical operations without altering the relational algebra. This algebra is referred to as statistical relational algebra.
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We will outline some of the important problems of extending the relational model for processing statistical data bases. We achieve this by augmenting to relational algebra few more numerical operations without altering the relational algebra. This algebra is referred to as statistical relational algebra.
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Statistical Models for Fracture
1998Recent developments in statistical physics studying fracture phenomena are reviewed. A quantity of experimental interest is the breaking characteristics of the system (force vs. displacement): we discuss its universal scaling behaviour. Moreover, the distribution of local strain has multifractal scaling properties just before the system breaks fully ...
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A statistical model in palynology
Geoscience and Man, 1971Abstract The causes of areal and temporal variation in palynofloras extracted from sedimentary rocks are divided into major sources of variation and extraneous sources of variation. Major sources include: (1) Time; (2) Climate; (3) Plant succession; (4) Variations in local weather conditions; (5) Area dominated by species; (6) Distance transported; (7)
Raymond A. Christopher, George F. Hart
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Statistical Model for Japanese Abbreviations
Intelligent Data Analysis, 2008We present a new approach to detect abbreviations given a root expression. The method is based on a statistical model combining two internal models: a generation and a verification model. The statistical model accounts for both the validity of abbreviations as a character sequence generated from a root (as learnt from the collection of abbreviation ...
Norifumi Murayama, Manabu Okumura
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Statistics for Model Calibration
2015Mathematical models of dynamic processes contain parameters which have to be estimated based on time-resolved experimental data. This task is often approached by optimization of a suitably chosen objective function. Maximization of the likelihood, i.e.
Kreutz, Clemens +2 more
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