Parametric model, the Glossary

Table of Contents

1. 25 relations: Binomial distribution, Cambridge University Press, Cardinality, Continuum (set theory), Cumulative distribution function, De Gruyter, Exponential family, Identifiability, Location–scale family, Nonparametric statistics, Normal distribution, Nuisance parameter, Parameter space, Parametric family, Parametric statistics, Poisson distribution, Prentice Hall, Probability density function, Probability distribution, Probability mass function, Sample space, Semiparametric model, Statistical model, Statistical model specification, Statistics.

2. Parametric statistics
3. Statistical models

Binomial distribution

In probability theory and statistics, the binomial distribution with parameters and is the discrete probability distribution of the number of successes in a sequence of independent experiments, each asking a yes–no question, and each with its own Boolean-valued outcome: success (with probability) or failure (with probability).

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Cambridge University Press

Cambridge University Press is the university press of the University of Cambridge.

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Cardinality

In mathematics, the cardinality of a set is a measure of the number of elements of the set.

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Continuum (set theory)

In the mathematical field of set theory, the continuum means the real numbers, or the corresponding (infinite) cardinal number, denoted by \mathfrak.

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Cumulative distribution function

In probability theory and statistics, the cumulative distribution function (CDF) of a real-valued random variable X, or just distribution function of X, evaluated at x, is the probability that X will take a value less than or equal to x. Every probability distribution supported on the real numbers, discrete or "mixed" as well as continuous, is uniquely identified by a right-continuous monotone increasing function (a càdlàg function) F \colon \mathbb R \rightarrow satisfying \lim_F(x).

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De Gruyter

Walter de Gruyter GmbH, known as De Gruyter, is a German scholarly publishing house specializing in academic literature.

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Exponential family

In probability and statistics, an exponential family is a parametric set of probability distributions of a certain form, specified below.

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Identifiability

In statistics, identifiability is a property which a model must satisfy for precise inference to be possible.

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Location–scale family

In probability theory, especially in mathematical statistics, a location–scale family is a family of probability distributions parametrized by a location parameter and a non-negative scale parameter. Parametric model and location–scale family are parametric statistics.

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Nonparametric statistics

Nonparametric statistics is a type of statistical analysis that makes minimal assumptions about the underlying distribution of the data being studied.

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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.

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Nuisance parameter

In statistics, a nuisance parameter is any parameter which is unspecified but which must be accounted for in the hypothesis testing of the parameters which are of interest.

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Parameter space

The parameter space is the space of possible parameter values that define a particular mathematical model.

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Parametric family

In mathematics and its applications, a parametric family or a parameterized family is a family of objects (a set of related objects) whose differences depend only on the chosen values for a set of parameters.

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Parametric statistics

Parametric statistics is a branch of statistics which leverages models based on a fixed (finite) set of parameters.

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Poisson distribution

In probability theory and statistics, the Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time if these events occur with a known constant mean rate and independently of the time since the last event.

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Prentice Hall

Prentice Hall was a major American educational publisher.

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Probability density function

In probability theory, a probability density function (PDF), density function, or density of an absolutely 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 be equal to that sample.

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Probability distribution

In probability theory and statistics, a probability distribution is the mathematical function that gives the probabilities of occurrence of possible outcomes for an experiment.

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Probability mass function

In probability and statistics, a probability mass function (sometimes called probability function or frequency function) is a function that gives the probability that a discrete random variable is exactly equal to some value.

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Sample space

In probability theory, the sample space (also called sample description space, possibility space, or outcome space) of an experiment or random trial is the set of all possible outcomes or results of that experiment.

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Semiparametric model

In statistics, a semiparametric model is a statistical model that has parametric and nonparametric components.

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Statistical model

A statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger population). Parametric model and statistical model are statistical models.

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Statistical model specification

In statistics, model specification is part of the process of building a statistical model: specification consists of selecting an appropriate functional form for the model and choosing which variables to include. Parametric model and statistical model specification are statistical models.

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Statistics

Statistics (from German: Statistik, "description of a state, a country") is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data.

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See also

Parametric statistics

• Analysis of variance
• Biweight midcorrelation
• Confidence distribution
• D'Agostino's K-squared test
• Fraction of variance unexplained
• German tank problem
• Group family
• Least-angle regression
• Linear regression
• Location–scale family
• Multi-attribute global inference of quality
• Normality test
• Ordinary least squares
• Parametric model
• Parametric statistics
• Pareto interpolation
• Pearson correlation coefficient
• Simple linear regression
• Student's t-test
• T-statistic

Statistical models

• ACE model
• All models are wrong
• Autologistic actor attribute models
• Bradley–Terry model
• Completely randomized design
• Control function (econometrics)
• Data-driven model
• Econometric models
• Energy-based model
• Exponential dispersion model
• Flow-based generative model
• Generative model
• Gilbert tessellation
• Graphical models
• Hurdle model
• Impartial culture
• Infinitesimal model
• Land use regression model
• Marginal structural model
• Mediation (statistics)
• Model selection
• Moderated mediation
• Nonlinear modelling
• Parametric model
• Phenomenological model
• Predictive modelling
• Q-RASAR
• Rasch model
• Reification (statistics)
• Relative likelihood
• Response modeling methodology
• Rubin causal model
• Statistical Modelling Society
• Statistical model
• Statistical model specification
• Statistical model validation
• Stochastic models
• Whittle likelihood

References

[1] https://en.wikipedia.org/wiki/Parametric_model

Also known as Parametric statistical model, Regular parametric model.