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Hájek–Le Cam Convolution Theorem
In statistics, the Hájek–Le Cam convolution theorem states that any regular estimator in a parametric model is asymptotically equivalent to a sum of two independent random variables, one of which is normal with asymptotic variance equal to the inverse of Fisher information, and the other having arbitrary distribution. The obvious corollary from this theorem is that the "best" among regular estimators are those with the second component identically equal to zero. Such estimators are called efficient and are known to always exist for regular parametric models. The theorem is named after Jaroslav Hájek and Lucien Le Cam. Statement Let ℘ = be a regular parametric model, and ''q''(''θ''): Θ → ℝ''m'' be a parameter in this model (typically a parameter is just one of the components of vector ''θ''). Assume that function ''q'' is differentiable on Θ, with the ''m × k'' matrix of derivatives denoted as ''q̇θ''. Define : I_^ = \dot(\theta) ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistics
Statistics (from German language, German: ', "description of a State (polity), state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. In applying statistics to a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such as "all people living in a country" or "every atom composing a crystal". Statistics deals with every aspect of data, including the planning of data collection in terms of the design of statistical survey, surveys and experimental design, experiments. When census data (comprising every member of the target population) cannot be collected, statisticians collect data by developing specific experiment designs and survey sample (statistics), samples. Representative sampling assures that inferences and conclusions can reasonably extend from the sample ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Regular Estimator
Regular estimators are a class of statistical estimators that satisfy certain regularity conditions which make them amenable to asymptotic analysis. The convergence of a regular estimator's distribution is, in a sense, locally uniform. This is often considered desirable and leads to the convenient property that a small change in the parameter does not dramatically change the distribution of the estimator.Vaart AW van der. Asymptotic Statistics. Cambridge University Press; 1998. Definition An estimator \hat_n of \psi(\theta) based on a sample of size n is said to be regular if for every h: \sqrt n \left ( \hat_n - \psi (\theta + h/\sqrt n) \right ) \stackrel L_\theta where the convergence is in distribution under the law of \theta + h/\sqrt n. L_\theta is some asymptotic distribution (usually this is a normal distribution with mean zero and variance which may depend on \theta). Examples of non-regular estimators Both the Hodges' estimator and the James-Stein estimatorBeran ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Parametric Model
In statistics, a parametric model or parametric family or finite-dimensional model is a particular class of statistical models. Specifically, a parametric model is a family of probability distributions that has a finite number of parameters. Definition A statistical model is a collection of probability distributions on some sample space. We assume that the collection, , is indexed by some set . The set is called the parameter set or, more commonly, the parameter space. For each , let denote the corresponding member of the collection; so is a cumulative distribution function. Then a statistical model can be written as : \mathcal = \big\. The model is a parametric model if for some positive integer . When the model consists of absolutely continuous distributions, it is often specified in terms of corresponding probability density functions: : \mathcal = \big\. Examples * The Poisson family of distributions is parametrized by a single number : : ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Independence (probability Theory)
Independence is a fundamental notion in probability theory, as in statistics and the theory of stochastic processes. Two event (probability theory), events are independent, statistically independent, or stochastically independent if, informally speaking, the occurrence of one does not affect the probability of occurrence of the other or, equivalently, does not affect the odds. Similarly, two random variables are independent if the realization of one does not affect the probability distribution of the other. When dealing with collections of more than two events, two notions of independence need to be distinguished. The events are called Pairwise independence, pairwise independent if any two events in the collection are independent of each other, while mutual independence (or collective independence) of events means, informally speaking, that each event is independent of any combination of other events in the collection. A similar notion exists for collections of random variables. M ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Normal Distribution
In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is f(x) = \frac e^\,. The parameter is the mean or expectation of the distribution (and also its median and mode), while the parameter \sigma^2 is the variance. The standard deviation of the distribution is (sigma). A random variable with a Gaussian distribution is said to be normally distributed, and is called a normal deviate. Normal distributions are important in statistics and are often used in the natural and social sciences to represent real-valued random variables whose distributions are not known. Their importance is partly due to the central limit theorem. It states that, under some conditions, the average of many samples (observations) of a random variable with finite mean and variance is itself a random variable—whose distribution c ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Fisher Information
In mathematical statistics, the Fisher information is a way of measuring the amount of information that an observable random variable ''X'' carries about an unknown parameter ''θ'' of a distribution that models ''X''. Formally, it is the variance of the score, or the expected value of the observed information. The role of the Fisher information in the asymptotic theory of maximum-likelihood estimation was emphasized and explored by the statistician Sir Ronald Fisher (following some initial results by Francis Ysidro Edgeworth). The Fisher information matrix is used to calculate the covariance matrices associated with maximum-likelihood estimates. It can also be used in the formulation of test statistics, such as the Wald test. In Bayesian statistics, the Fisher information plays a role in the derivation of non-informative prior distributions according to Jeffreys' rule. It also appears as the large-sample covariance of the posterior distribution, provided that the prior i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Jaroslav Hájek
Jaroslav Hájek (; 1926–1974) was a Czech people, Czech mathematician who worked in theoretical statistics, theoretical and nonparametric statistics, nonparametric statistics. The Hajek projection and Hájek–Le Cam convolution theorem are named after him (as well as collaborator Lucien Le Cam). Life Jaroslav Hájek studied statistical and insurance engineering at the Faculty of Special Sciences of the Czech Technical University in Prague, Czech Technical University in Prague and in 1950 he successfully completed this study by obtaining an engineering degree. In 1955 he received the title of CSc. for the paper Contributions to the theory of statistical estimation, the supervisor of this thesis was Josef Novák. In 1963, he received a D.Sc. in the same year he received his habilitation at the Faculty of Mathematics and Physics of Charles University, in 1966 he was entitled professor at this faculty. In 1973, he was awarded the Klement Gottwald State Prize for his work on the asym ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Lucien Le Cam
Lucien Marie Le Cam (November 18, 1924 – April 25, 2000) was a mathematician and statistician. Biography Le Cam was born November 18, 1924, in Croze, France. His parents were farmers, and unable to afford higher education for him; his father died when he was 13. After graduating from a Catholic school in 1942, he began studying at a seminary in Limoges, but immediately quit upon learning that he would not be allowed to study chemistry there. Instead he continued his studies at a lycée, which did not teach chemistry but did teach mathematics. In May 1944 he joined an underground group, and then went into hiding, returning to his school the following November but soon afterwards moving to Paris, where he began studying at the University of Paris. He graduated in 1945 with the degree '' licence ès sciences''.. Le Cam then worked for a hydroelectric utility for five years, while meeting at the University of Paris for a weekly seminar in statistics. In 1950, he was invited to beco ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Score (statistics)
In statistics, the score (or informant) is the gradient of the log-likelihood function with respect to the statistical parameter, parameter vector. Evaluated at a particular value of the parameter vector, the score indicates the steepness of the log-likelihood function and thereby the sensitivity to infinitesimal changes to the parameter values. If the log-likelihood function is Continuous function, continuous over the parameter space, the score will vanish (mathematics), vanish at a local Maxima and minima, maximum or minimum; this fact is used in maximum likelihood estimation to find the parameter values that maximize the likelihood function. Since the score is a function of the Realization (probability), observations, which are subject to sampling error, it lends itself to a test statistic known as ''score test'' in which the parameter is held at a particular value. Further, the likelihood ratio, ratio of two likelihood functions evaluated at two distinct parameter values can ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Matrix Transpose
In linear algebra, the transpose of a matrix is an operator which flips a matrix over its diagonal; that is, it switches the row and column indices of the matrix by producing another matrix, often denoted by (among other notations). The transpose of a matrix was introduced in 1858 by the British mathematician Arthur Cayley. Transpose of a matrix Definition The transpose of a matrix , denoted by , , , A^, , , or , may be constructed by any one of the following methods: # Reflect over its main diagonal (which runs from top-left to bottom-right) to obtain #Write the rows of as the columns of #Write the columns of as the rows of Formally, the -th row, -th column element of is the -th row, -th column element of : :\left mathbf^\operatorname\right = \left mathbf\right. If is an matrix, then is an matrix. In the case of square matrices, may also denote the th power of the matrix . For avoiding a possible confusion, many authors use left upperscripts, t ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Convergence In Distribution
In probability theory, there exist several different notions of convergence of sequences of random variables, including ''convergence in probability'', ''convergence in distribution'', and ''almost sure convergence''. The different notions of convergence capture different properties about the sequence, with some notions of convergence being stronger than others. For example, convergence in distribution tells us about the limit distribution of a sequence of random variables. This is a weaker notion than convergence in probability, which tells us about the value a random variable will take, rather than just the distribution. The concept is important in probability theory, and its applications to statistics and stochastic processes. The same concepts are known in more general mathematics as stochastic convergence and they formalize the idea that certain properties of a sequence of essentially random or unpredictable events can sometimes be expected to settle down into a behavior that ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
