Estimator and Expected value

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Difference between Estimator and Expected value

Estimator vs. Expected value

In statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule (the estimator), the quantity of interest (the estimand) and its result (the estimate) are distinguished. In probability theory, the expected value of a random variable, intuitively, is the long-run average value of repetitions of the experiment it represents.

Bias of an estimator

In statistics, the bias (or bias function) of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated.

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Central tendency

In statistics, a central tendency (or measure of central tendency) is a central or typical value for a probability distribution.

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Decision theory

Decision theory (or the theory of choice) is the study of the reasoning underlying an agent's choices.

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Errors and residuals

In statistics and optimization, errors and residuals are two closely related and easily confused measures of the deviation of an observed value of an element of a statistical sample from its "theoretical value".

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Estimation theory

Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component.

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Loss function

In mathematical optimization, statistics, econometrics, decision theory, machine learning and computational neuroscience, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event.

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Probability density function

In probability theory, a probability density function (PDF), or density of a continuous random variable, is a function, whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the random variable would equal that sample.

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Random variable

In probability and statistics, a random variable, random quantity, aleatory variable, or stochastic variable is a variable whose possible values are outcomes of a random phenomenon.

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Sample size determination

Sample size determination is the act of choosing the number of observations or replicates to include in a statistical sample.

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Variance

In probability theory and statistics, variance is the expectation of the squared deviation of a random variable from its mean.

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