Substance theory

2014-01-20T14:04:00Z

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A '''statistical model''' is a formalization of relationships between variables in the form of mathematical equations. A statistical model describes how one or more [[random variables]] are related to one or more other variables. The model is statistical as the variables are not [[Deterministic system|deterministically]] but [[stochastic]]ally related. In mathematical terms, a statistical model is frequently thought of as a pair <math> (Y, P) </math> where <math> Y </math> is the set of possible observations and <math> P </math> the set of possible [[probability distributions]] on <math> Y </math>. It is assumed that there is a distinct element of <math> P </math> which generates the observed data. [[Statistical inference]] enables us to make statements about which element(s) of this set are likely to be the true one.

Most statistical tests can be described in the form of a statistical model. For example, the [[Student's t-test]] for comparing the means of two groups can be formulated as seeing if an estimated [[parameter]] in the model is different from 0. Another similarity between tests and models is that there are assumptions involved. Error is assumed to be normally distributed in most models.<ref>Field, A. (2005). Discovering statistics using SPSS. Sage, London.</ref>

==Formal definition==

A statistical model is a collection of [[Cumulative distribution function|probability distribution functions]] or [[probability density function]]s (collectively referred to as ''distributions'' for brevity). A [[parametric model]] is a collection of distributions, each of which is indexed by a unique finite-dimensional parameter: <math>\mathcal{P}=\{\mathbb{P}_{\theta} : \theta \in \Theta\}</math>, where <math>\theta</math> is a parameter and <math>\Theta \subseteq \mathbb{R}^d</math> is the feasible region of parameters, which is a subset of d-dimensional [[Euclidean space]]. A statistical model may be used to describe the set of distributions from which one assumes that a particular data set is sampled. For example, if one assumes that data arise from a univariate [[Gaussian distribution]], then one has assumed a Gaussian model
:<math>\mathcal{P}=\{\mathbb{P}(x; \mu, \sigma) = \frac{1}{\sqrt{2 \pi} \sigma} \exp\left\{ -\frac{1}{2\sigma^2}(x-\mu)^2\right\} : \mu \in \mathbb{R}, \sigma > 0\}</math>.

A [[non-parametric model]] is a set of probability distributions with infinite dimensional parameters, and might be written as <math>\mathcal{P}=\{\text{all distributions}\}</math>. A [[semi-parametric model]] also has infinite dimensional parameters, but is not dense in the space of distributions. For example, a mixture of Gaussians with one Gaussian at each data point is dense in the space of distributions. Formally, if d is the dimension of the parameter, and n is the number of samples, if <math>d \rightarrow \infty</math> as <math>n \rightarrow \infty</math> and <math>d/n \rightarrow 0</math> as <math>n \rightarrow \infty</math>, then the model is semi-parametric.

==Model comparison==
Models can be compared to each other. This can either be done when you have done an [[exploratory data analysis]] or a [[confirmatory data analysis]]. In an exploratory analysis, you formulate all models you can think of, and see which describes your data best. In a confirmatory analysis you test which of your models you have described before the data was collected fits the data best, or test if your only model fits the data. In linear regression analysis you can compare the amount of variance explained by the independent variables, R2, across the different models. In general, you can compare models that are nested by using a [[Likelihood-ratio test]]. Nested models are models that can be obtained by restricting a parameter in a more complex model to be zero.

==An example==
Height and age are probabilistically distributed over humans. They are stochastically related; when you know that a person is of age 7, this influences the chance of this person being 6 feet tall. You could formalize this relationship in a [[linear regression model]] of the following form:
heighti = b0 + b1agei + εi, where b0 is the intercept, b1 is a parameter that age is multiplied by to get a prediction of height, ε is the error term, and i is the subject. This means that height starts at some value, there is a minimum height when someone is born, and it is predicted by age to some amount. This prediction is not perfect as error is included in the model. This error contains variance that stems from sex and other variables. When sex is included in the model, the error term will become smaller, as you will have a better idea of the chance that a particular 16-year-old is 6 feet tall when you know this 16-year-old is a girl. The model would become heighti = b0 + b1agei + b2sexi + εi, where the variable sex is [[dichotomous]]. This model would presumably have a higher R2. The first model is nested in the second model: the first model is obtained from the second when b2 is restricted to zero.

==Classification==
According to the number of the endogenous variables and the number of equations, models can be classified as complete models (the number of equations equal to the number of endogenous variables) and incomplete models. Some other statistical models are the [[general linear model]] (restricted to continuous dependent variables), the [[generalized linear model]] (for example, [[logistic regression]]), the [[multilevel model]], and the [[structural equation model]].<ref>[[Adèr, H.J.]] (2008). Chapter 12: Modelling. In [[H.J. Adèr]] & [[Gideon J. Mellenbergh|G.J. Mellenbergh]] (Eds.) (with contributions by D.J. Hand), Advising on Research Methods: A consultant's companion (pp. 271-304). Huizen, The Netherlands: Johannes van Kessel Publishing.</ref>

==See also==
{{Commons category|{{PAGENAME}}}}
*[[A/B testing]]
*[[Econometric model]]
*[[Mathematical diagram]]
*[[Regression analysis]]

{{More footnotes|date=September 2010}}

==References==
<references/>

{{DEFAULTSORT:Statistical Model}}
[[Category:Statistical models| ]]
[[Category:Statistical theory]]
[[Category:Scientific modeling]]

Job scheduling game

2013-12-12T13:37:39Z

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In [[field theory (mathematics)|field theory]], a branch of algebra, a [[field extension]] <math>L/k</math> is said to be '''regular''' if ''k'' is [[algebraically closed]] in ''L'' {{Clarification needed|date=July 2013}} and ''L'' is [[separable extension|separable]] over ''k'', or equivalently, <math>L \otimes_k \overline{k}</math> is an integral domain when <math>\overline{k}</math> is the algebraic closure of <math>k</math> (that is, to say, <math>L, \overline{k}</math> are [[linearly disjoint]] over ''k'').<ref name=FJ38>Fried & Jarden (2008) p.38</ref><ref name=C425>Cohn (2003) p.425</ref>

==Properties==
* Regularity is transitive: if ''F''/''E'' and ''E''/''K'' are regular then so is ''F''/''K''.<ref name=FJ39>Fried & Jarden (2008) p.39</ref>
* If ''F''/''K'' is regular then so is ''E''/''K'' for any ''E'' between ''F'' and ''K''.<ref name=FJ39/>
* The extension ''L''/''k'' is regular if and only if every subfield of ''L'' finitely generated over ''k'' is regular over ''k''.<ref name=C425/>
* Any extension of an algebraically closed field is regular.<ref name=FJ39/><ref name=C426>Cohn (2003) p.426</ref>
* An extension is regular if and only if it is separable and [[primary extension|primary]].<ref name=FJ44>Fried & Jarden (2008) p.44</ref>
* A [[purely transcendental extension]] of a field is regular.

==Self-regular extension==
There is also a similar notion: a field extension <math>L / k</math> is said to be '''self-regular''' if <math>L \otimes_k L</math> is an integral domain. A self-regular extension is relatively algebraically closed in ''k''.<ref name=C427>Cohn (2003) p.427</ref> However, a self-regular extension is not necessarily regular.{{Citation needed|date=February 2010}}

==References==
{{reflist}}
* {{cite book | last1=Fried | first1=Michael D. | last2=Jarden | first2=Moshe | title=Field arithmetic | edition=3rd revised | series=Ergebnisse der Mathematik und ihrer Grenzgebiete. 3. Folge | volume=11 | publisher=[[Springer-Verlag]] | year=2008 | isbn=978-3-540-77269-9 | zbl=1145.12001 | pages=38-41 }}
* M. Nagata (1985). Commutative field theory: new edition, Shokado. (Japanese) [http://www.shokabo.co.jp/mybooks/ISBN978-4-7853-1309-8.htm]
* {{cite book | title=Basic Algebra. Groups, Rings, and Fields | first=P. M. | last=Cohn | authorlink=Paul Cohn | publisher=[[Springer-Verlag]] | year=2003 | isbn=1-85233-587-4 | zbl=1003.00001 }}
* A. Weil, [[Foundations of algebraic geometry]].

[[Category:Field theory]]

{{Abstract-algebra-stub}}

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Substance theory

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