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Postmarketing surveillance (PMS), also known as post market surveillance, is the practice of monitoring the safety of a pharmaceutical drug or medical device after it has been released on the market and is an important part of the science of pharmacovigilance.
Model selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. [1] In the context of machine learning and more generally statistical analysis , this may be the selection of a statistical model from a set of candidate models, given data.
An occupational safety management system (OSMS) is a management system designed to manage occupational safety and health risks in the workplace.If the system contains elements of management of longer-term health impacts and occupational disease, it may be referred to as a occupational safety and health management system (OSHMS) or occupational health and safety management system (OHSMS).
In statistics, Mallows's, [1] [2] named for Colin Lingwood Mallows, is used to assess the fit of a regression model that has been estimated using ordinary least squares.It is applied in the context of model selection, where a number of predictor variables are available for predicting some outcome, and the goal is to find the best model involving a subset of these predictors.
In efficient quantile regression, an EL-based categorization [9] procedure helps determine the shape of the true discrete distribution at level p, and also provides a way of formulating a consistent estimator. In addition, EL can be used in place of parametric likelihood to form model selection criteria. [10]
Similarly, for a regression analysis, an analyst would report the coefficient of determination (R 2) and the model equation instead of the model's p-value. However, proponents of estimation statistics warn against reporting only a few numbers. Rather, it is advised to analyze and present data using data visualization.
When the statistical model has several parameters, however, the mean of the parameter-estimator is a vector and its variance is a matrix. The inverse matrix of the variance-matrix is called the "information matrix". Because the variance of the estimator of a parameter vector is a matrix, the problem of "minimizing the variance" is complicated.
First, with a data sample of length n, the data analyst may run the regression over only q of the data points (with q < n), holding back the other n – q data points with the specific purpose of using them to compute the estimated model’s MSPE out of sample (i.e., not using data that were used in the model estimation process).