Results 301 to 310 of about 26,575,973 (337)
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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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‘Statistics-for’ and ‘Statistics-with’ Agent Models
2021Humankind often sees patterns where none exist (The name for this is pareidolia and it explains among other things why we see faces on the surface of the Moon and Mars, and can make out cars, cats and coffee cups among the clouds.). Statistical tests exist to overcome this and reveal true relationships among variables while quantifying their strength ...
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Statistics for a Diagnostic Model
Biometrics, 1961In recent years, several methods have been proposed for making medical diagnoses by machine (Ledley and Lusted [1959], Crumb and Rupe [1959]). A method devised by Brodman et al. [1959, 1960] has been used to program a high-speed electronic computer for making presumptive medical diagnoses using only information relating to the age, sex, and responses ...
Keeve Brodman, Adrianus J. van Woerkom
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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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On regularity for statistical models
Canadian Journal of Statistics, 1985AbstractSome recent discussions of the logic involved in statistical inference have focussed on the given (i. e. the statistical model and the data) and the role of the common reduction principles (namely conditionality, likelihood, and sufficiency). The minimum statistical model, a class of probability measures on a measurable space, can yield many ...
G. Monette, Michael Evans, Donald Fraser
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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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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 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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2020
This chapter covers some of the basics of time series statistics as they relate to our model. An analytic solution to our model is derived with log preferences. Then the chapter discusses how to program up the model and compute sample statistic from model simulations.
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This chapter covers some of the basics of time series statistics as they relate to our model. An analytic solution to our model is derived with log preferences. Then the chapter discusses how to program up the model and compute sample statistic from model simulations.
openaire +1 more source