Estimation theory

statistical approach for modeling the relationship between a scalar dependent variable and one or more explanatory variables

thumb|right|The exact number of candies in this jar cannot be determined by looking at it, because most of the candies are not visible. It can be estimated by assuming that the density of the unseen candies is the same as that of the visible candies. Estimation (or estimating) is the process of finding an estimate or approximation, which is a value that is usable for some purpose even if input data may be incomplete, uncertain, or unstable. The value is nonetheless usable because it is derived from the best information available. Typically, estimation involves "using the value of a statistic d

way of measuring the amount of information that an observable random variable carries about an unknown parameter of a distribution that models it

branch of statistics to estimate models based on measured data

lower bound on variance of an estimator

technique for updating numerical model with observed data

parameter estimation via sample statistics

theorem

quality measure of a statistical method

theoretical framework for machine learning

concept in econometrics

space of possible parameter values that define a particular mathematical model

ratio of the number of outcomes in which a specified event occurs to the total number of trials

statistical parameter needed for a model but not of primary interest

measure to compare interventions in randomized experiments; the difference in mean outcomes between treatment units and control units

theorem

In statistical theory, a U-statistic is a class of statistics defined as the average over the application of a given function applied to all tuples of a fixed size. The letter "U" stands for unbiased. In elementary statistics, U-statistics arise naturally in producing minimum-variance unbiased estimators.

phenomenon of joint estimation sometimes being strictly better than serial estimation across parameters

In statistics, identifiability is a property which a model must satisfy for precise inference to be possible. A model is identifiable if it is theoretically possible to learn the true values of this model's underlying parameters after obtaining an infinite number of observations from it. Mathematically, this is equivalent to saying that different values of the parameters must generate different probability distributions of the observable variables. Usually the model is identifiable only under certain technical restrictions, in which case the set of these requirements is called the identificati