Background and aims: Since accurate forecasts help inform decisions for preventive health-careintervention and epidemic control, this goal can only be achieved by making use of appropriatetechniques and methodologies. As much as forecast precision is important, methods and modelselection procedures are critical to forecast precision. This study aimed at providing an overview o...

The purpose of this paper is to explain the interest and importance of (approximate) models and model selection in Statistics. Starting from the very elementary example of histograms we present a general notion of finite dimensional model for statistical estimation and we explain what type of risk bounds can be expected from the use of one such model. We then give the performance of suitable mo...

Background: Selection of human spermatozoa prior to assisted reproduction is currently based on motility and morphology. Recently, Zeta method has been introduced for normal sperm selection based on sperm surface charges. In addition, one of the features of apoptosis is the externalization of phosphatidylserine (PS), which are normally present on the inner leaflet of the sperm plasma membrane. ...

We consider the problem of model selection in unsupervised statistical learning techniques based on low-rank matrix approximations. While k-fold crossvalidation (CV) has become the standard method of choice for model selection in supervised learning techniques, its adaptation to unsupervised matrix approximation settings has not received sufficient attention in the literature. In this paper, we...

We analyze general model selection procedures using penalized empirical loss minimization under computational constraints. While classical model selection approaches do not consider computational aspects of performing model selection, we argue that any practical model selection procedure must not only trade off estimation and approximation error, but also the effects of the computational effort...

This paper examines the possibilities of carrying out unbiased evaluations for compensatory education programs when group selection is not random and/or when pretests are unavailable. Four models are presented which demonstrate that bias can be avoided if particular selection procedures are used. The models are then considered for their usefulness in evaluating Head Start by using the data coll...

To compute norms from reference group test scores, continuous norming is preferred over traditional norming. A suitable continuous norming approach for continuous data is the use of the Box–Cox Power Exponential model, which is found in the generalized additive models for location, scale, and shape. Applying the Box–Cox Power Exponential model for test norming requires model selection, but it i...

Operator procedures are documents telling operators what to do in various situations. They are widely used in process industries including the nuclear power industry. The correctness of such procedures is of great importance. We describe how model checking can be used to detect potential errors and to verify properties of operator procedures. As there could be problems with modelling and model ...

It is common in modern applications to use data-dependent model selection tools to select a promising model before drawing inference over the parameters of the selected model. However, this simple series of steps conceals a significant fault that is often left unattended: the act of selection biases the distributions of test statistics and makes standard inference procedures unsound. This is re...