KaKs_Calculator is a software package that calculates nonsynonymous (Ka) and synonymous (Ks) substitution rates through model selection and model averaging. Since existing methods for this estimation adopt their specific mutation (substitution) models that consider different evolutionary features, leading to diverse estimates, KaKs_Calculator implements a set of candidate models in a maximum li...

This paper addressees the problem of an early diagnosis of Parkinson’s disease by the classification of characteristic features of person’s voice. A new, two-step classification approach is proposed. In the first step, the voice samples are classified using standard state-of-the-art classifiers. In the second step, the classified samples are assigned to patients and the final classification pro...

Akaike’s criterion is often used to test composite hypotheses; for example to determine the order of a priori unknown Auto-Regressive and/or Moving Average models. Objections are formulated against Akaike’s criterion and some modifications are proposed. The application of the theory leads to a general technique for AR-model order estimation based on testing pairs of composite hypotheses. This t...

A typical approach to the problem of selecting between models of differing complexity is to choose the model with the minimum Akaike Information Criterion (AIC) score. This paper examines a common scenario in which there is more than one candidate model with the same number of free parameters which violates the conditions under which AIC was derived. The main result of this paper is a novel upp...

Nonparametric tests for the null hypothesis that a function has a prescribed form are developed and applied to data sets with missing observations. Omnibus nonparametric tests such as the order selection tests, do not need to specify a particular alternative parametric form, and have power against a large range of alternatives. More specifically, likelihood-based order selection tests are defin...

The degrees of freedom of semiparametric additive monotone models are derived using results about projections onto sums of order cones. Two important related questions are also studied, namely, the definition of estimators for the parameter of the error term and the formulation of specific Akaike Information Criteria statistics. Several alternatives are proposed to solve both problems and simul...

We address the problem of estimating some unknown density on a bounded interval using some exponential models of piecewise polynomials. We consider a finite collection of such models based on a family of partitions. And we study the maximum-likelihood estimator built on a data-driven selected model among this collection. In doing so, we validate Akaike’s criterion if the partitions that we cons...

Amaximization of the expected entropy of the predictive distribution interpretation of Akaike's minimum AIC procedure is exploited for the modeling and prediction of time series with trend and seasonal mean value functions and stationary covariances. The AIC criterion best one-step-ahead and best twelvestep-ahead prediction models are different. They exhibit the relative optimality properties f...

Previous studies have demonstrated that non-Euclidean distance metrics can improve model fit in the geographically weighted regression (GWR) model. However, the GWR model often considers spatial nonstationarity and does not address variations in local temporal issues. Therefore, this paper explores a geographically temporal weighted regression (GTWR) approach that accounts for both spatial and ...

Comparing self-assessed indicators of subjective outcomes such as health, work disability, political efficacy, job satisfaction, etc. across countries or socio-economic groups is often hampered by the fact that different groups use systematically different response scales. Anchoring vignettes have been introduced as an effective tool to correct for such differences. This paper develops an integ...