Cross-validation of methods is an essential component of genome-enabled prediction of complex traits. We develop formulae for computing the predictions that would be obtained when one or several cases are removed in the training process, to become members of testing sets, but by running the model using all observations only once. Prediction methods to which the developments apply include least ...

We consider the problem of finding isolated common roots a set polynomial functions defining zero-dimensional ideal I in ring R polynomials over C. Normal form algorithms provide an algebraic approach to solve this problem. The framework presented Telen et al. (2018) uses truncated normal forms (TNFs) compute algebra structure R/I and solutions I. This allows for use much more general bases tha...

In this paper, we present simple explicit expressions for single and product moments of generalized order statistics from Pareto distribution. These relations are deduced the record values tabulated mean variance Further, conditional expectation, recurrence as well truncated moment used to characterize

We present Conrad, the first comparative gene predictor based on semi-Markov conditional random fields (SMCRFs). Unlike the best standalone gene predictors, which are based on generalized hidden Markov models (GHMMs) and trained by maximum likelihood, Conrad is discriminatively trained to maximize annotation accuracy. In addition, unlike the best annotation pipelines, which rely on heuristic an...

OBJECTIVE To help facilitate economic evaluations of oncology treatments, we mapped responses on cancer-specific instrument to generic preference-based measures. METHODS Cancer patients (n = 367) completed one cancer-specific instrument, the FACT-G, and two preference-based measures, the EQ-5D and SF-6D. Responses were randomly divided to form development (n = 184) and cross-validation (n = 1...

Besides being an ill-posed problem, the pel-recursive computation of 2-D optical flow raises a wealth of issues, such as the treatment of outliers, motion discontinuities and occlusion. Our proposed approach deals with these issues within a common framework. It relies on the use of a data-driven technique called Generalized Cross Validation (GCV) to estimate the best regularization scheme for a...

Machine-learning algorithms have gained popularity in recent years in the field of ecological modeling due to their promising results in predictive performance of classification problems. While the application of such algorithms has been highly simplified in the last years due to their well-documented integration in commonly used statistical programming languages such as R, there are several pr...

Quality control in electronic publications should be one of the major concerns of everyone who is managing a project. Big projects, like digital libraries, try to gather information from a series of different sources: libraries, museums, universities, and other scientific or cultural organizations. Collecting and treating information from several different sources raises very interesting proble...

Understanding a phenomenon from observed data requires contextual and efficient statistical models. Such models are based on probability distributions having sufficiently flexible properties to adapt maximum of situations. Modern examples include the truncated Fréchet generated family. In this paper, we go even further by introducing more general family, version generalized distribution. This g...