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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.
It is closely related to non-identifiability in statistics and econometrics, which occurs when a statistical model has more than one set of parameters that generate the same distribution of observations, meaning that multiple parameterizations are observationally equivalent.
Structural methods are also referred to as a priori, because non-identifiability analysis in this case could also be performed prior to the calculation of the fitting score functions, by exploring the number degrees of freedom (statistics) for the model and the number of independent experimental conditions to be varied.
Problem discovery is an unconscious process which depends upon knowledge whereby an idea enters one's conscious awareness, problem formulation is the discovery of a goal; problem construction involves modifying a known problem or goal to another one; problem identification represents a problem that exists in reality but needs to be discovered ...
In statistics and econometrics, set identification (or partial identification) extends the concept of identifiability (or "point identification") in statistical models to environments where the model and the distribution of observable variables are not sufficient to determine a unique value for the model parameters, but instead constrain the parameters to lie in a strict subset of the ...
Sawilowsky [56] distinguishes between a simulation, a Monte Carlo method, and a Monte Carlo simulation: a simulation is a fictitious representation of reality, a Monte Carlo method is a technique that can be used to solve a mathematical or statistical problem, and a Monte Carlo simulation uses repeated sampling to obtain the statistical ...
In statistics, where classification is often done with logistic regression or a similar procedure, the properties of observations are termed explanatory variables (or independent variables, regressors, etc.), and the categories to be predicted are known as outcomes, which are considered to be possible values of the dependent variable.
In general the problem concerns both detecting whether or not a change has occurred, or whether several changes might have occurred, and identifying the times of any such changes. Specific applications, like step detection and edge detection , may be concerned with changes in the mean , variance , correlation , or spectral density of the process.