statistical model

Let $\textbf{X}=(X_{1},\ldots,X_{n})$ be a random vector with a given realization $\textbf{X}(\omega)=(x_{1},\ldots,x_{n})$, where $\omega$ is the outcome (of an observation or an experiment) in the sample space $\Omega$. A statistical model $\mathcal{P}$ based on X is a set of probability distribution functions of X:

 $\mathcal{P}=\{F_{\textbf{X}}\}.$

If it is known in advance that this family of distributions  comes from a set of continuous distributions, the statistical model $\mathcal{P}$ can be equivalently defined as a set of probability density functions:

 $\mathcal{P}=\{f_{\textbf{X}}\}.$

As an example, a coin is tossed $n$ times and the results are observed. The probability of landing a head during one toss is $p$. Assume that each toss is independent of one another. If $\textbf{X}=(X_{1},\ldots,X_{n})$ is defined to be the vector of the $n$ ordered outcomes, then a statistical model based on X can be a family of Bernoulli distributions  $\mathcal{P}=\{\prod_{i=1}^{n}p^{x_{i}}(1-p)^{1-x_{i}}\},$

where $X_{i}(\omega)=x_{i}$ and $x_{i}=1$ if $\omega$ is the outcome that the $i$th toss lands a head and $x_{i}=0$ if $\omega$ is the outcome that the $i$th toss lands a tail.

Next, suppose $X$ is the number of tosses where a head is observed, then a statistical model based on $X$ can be a family binomial distributions:

 $\mathcal{P}=\{{n\choose x}p^{x}(1-p)^{n-x}\},$

where $X(\omega)=x$, where $\omega$ is the outcome that $x$ heads (out of $n$ tosses) are observed.

A statistical model is usually parameterized by a function  , called a parameterization

 $\Theta\rightarrow\mathcal{P}\mbox{ given by }\theta\mapsto F_{\theta}\mbox{ so% that }\mathcal{P}=\{F_{\theta}\mid\theta\in\Theta\},$

where $\Theta$ is called a parameter space. $\Theta$ is usually a subset of $\mathbb{R}^{n}$. However, it can also be a function space.

In the first part of the above example, the statistical model is parameterized by

 $p\mapsto\prod_{i=1}^{n}p^{x_{i}}(1-p)^{1-x_{i}}.$

If the parameterization is a one-to-one function, it is called an identifiable parameterization and $\theta$ is called a parameter  . The $p$ in the above example is a parameter.

Title statistical model StatisticalModel 2013-03-22 14:33:18 2013-03-22 14:33:18 CWoo (3771) CWoo (3771) 10 CWoo (3771) Definition msc 62A01 identifiable parameterization parameter space