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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.
Heckman also developed a two-step control function approach to estimate this model, [3] which avoids the computational burden of having to estimate both equations jointly, albeit at the cost of inefficiency. [4] Heckman received the Nobel Memorial Prize in Economic Sciences in 2000 for his work in this field. [5]
In order to still use the Box–Jenkins approach, one could difference the series and then estimate models such as ARIMA, given that many commonly used time series (e.g. in economics) appear to be stationary in first differences. Forecasts from such a model will still reflect cycles and seasonality that are present in the data.
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.
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Multilevel regression with poststratification (MRP) is a statistical technique used for correcting model estimates for known differences between a sample population (the population of the data one has), and a target population (a population one wishes to estimate for).
This implies that different organizations benefit from different estimation approaches. Findings [19] that may support the selection of estimation approach based on the expected accuracy of an approach include: Expert estimation is on average at least as accurate as model-based effort estimation.
"Best linear unbiased estimation and prediction under a selection model". Biometrics. 31 (2): 423– 447. doi:10.2307/2529430. JSTOR 2529430. PMID 1174616. Liu, Xu-Qing; Rong, Jian-Ying; Liu, Xiu-Ying (2008). "Best linear unbiased prediction for linear combinations in general mixed linear models". Journal of Multivariate Analysis. 99 (8): 1503 ...