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32 relations: Bernoulli process, Bernoulli trial, Chain rule, Computational statistics, Confidence interval, Cramér–Rao bound, Epidemiology (journal), Estimator, Expected value, Fisher information, Gradient, Information theory, Leibniz integral rule, Likelihood function, Logarithm, Maximum likelihood estimation, Natural logarithm, Numerical analysis, Parametric model, Partial derivative, Probability density function, Score test, Scoring algorithm, Sensitivity analysis, Statistic, Statistical inference, Statistical regularity, Statistics, Stochastic process, Support curve, Test statistic, Variance.
Bernoulli process
In probability and statistics, a Bernoulli process is a finite or infinite sequence of binary random variables, so it is a discrete-time stochastic process that takes only two values, canonically 0 and 1.
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Bernoulli trial
In the theory of probability and statistics, a Bernoulli trial (or binomial trial) is a random experiment with exactly two possible outcomes, "success" and "failure", in which the probability of success is the same every time the experiment is conducted.
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Chain rule
In calculus, the chain rule is a formula for computing the derivative of the composition of two or more functions.
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Computational statistics
Computational statistics, or statistical computing, is the interface between statistics and computer science.
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Confidence interval
In statistics, a confidence interval (CI) is a type of interval estimate, computed from the statistics of the observed data, that might contain the true value of an unknown population parameter.
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Cramér–Rao bound
In estimation theory and statistics, the Cramér–Rao bound (CRB), Cramér–Rao lower bound (CRLB), Cramér–Rao inequality, Frechet–Darmois–Cramér–Rao inequality, or information inequality expresses a lower bound on the variance of unbiased estimators of a deterministic (fixed, though unknown) parameter.
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Epidemiology (journal)
Epidemiology is a bi-monthly, peer-reviewed journal for epidemiologic research, published by Lippincott Williams & Wilkins.
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Estimator
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.
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Expected value
In probability theory, the expected value of a random variable, intuitively, is the long-run average value of repetitions of the experiment it represents.
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Fisher information
In mathematical statistics, the Fisher information (sometimes simply called 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.
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Gradient
In mathematics, the gradient is a multi-variable generalization of the derivative.
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Information theory
Information theory studies the quantification, storage, and communication of information.
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Leibniz integral rule
In calculus, Leibniz's rule for differentiation under the integral sign, named after Gottfried Leibniz, states that for an integral of the form where -\infty, the derivative of this integral is expressible as where the partial derivative indicates that inside the integral, only the variation of f(x, t) with x is considered in taking the derivative.
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Likelihood function
In frequentist inference, a likelihood function (often simply the likelihood) is a function of the parameters of a statistical model, given specific observed data.
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Logarithm
In mathematics, the logarithm is the inverse function to exponentiation.
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Maximum likelihood estimation
In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a statistical model, given observations.
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Natural logarithm
The natural logarithm of a number is its logarithm to the base of the mathematical constant ''e'', where e is an irrational and transcendental number approximately equal to.
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Numerical analysis
Numerical analysis is the study of algorithms that use numerical approximation (as opposed to general symbolic manipulations) for the problems of mathematical analysis (as distinguished from discrete mathematics).
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Parametric model
In statistics, a parametric model or parametric family or finite-dimensional model is a family of distributions that can be described using a finite number of parameters.
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Partial derivative
In mathematics, a partial derivative of a function of several variables is its derivative with respect to one of those variables, with the others held constant (as opposed to the total derivative, in which all variables are allowed to vary).
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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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Score test
Rao's score test, also known as the score test or the Lagrange multiplier test (LM test) in econometrics, is a statistical test of a simple null hypothesis that a parameter of interest \theta is equal to some particular value \theta_0.
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Scoring algorithm
Scoring algorithm, also known as Fisher's scoring, is a form of Newton's method used in statistics to solve maximum likelihood equations numerically, named after Ronald Fisher.
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Sensitivity analysis
Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be apportioned to different sources of uncertainty in its inputs.
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Statistic
A statistic (singular) or sample statistic is a single measure of some attribute of a sample (e.g. its arithmetic mean value).
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Statistical inference
Statistical inference is the process of using data analysis to deduce properties of an underlying probability distribution.
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Statistical regularity
Statistical regularity is a notion in statistics and probability theory that random events exhibit regularity when repeated enough times or that enough sufficiently similar random events exhibit regularity.
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Statistics
Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.
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Stochastic process
--> In probability theory and related fields, a stochastic or random process is a mathematical object usually defined as a collection of random variables.
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Support curve
In statistics, a support curve is the graph of the natural logarithm of the likelihood function.
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Test statistic
A test statistic is a statistic (a quantity derived from the sample) used in statistical hypothesis testing.
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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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References
[1] https://en.wikipedia.org/wiki/Score_(statistics)
