Statistical analysis of autoregressive fractionally integrated moving average models in R

The autoregressive fractionally integrated moving average (ARFIMA) processes are one of the best-known classes of long-memory models. In the package afmtools for R, we have implemented a number of statistical tools for analyzing ARFIMA models. In particular, this package contains functions for parameter estimation, exact autocovariance calculation, predictive ability testing and impulse response function computation, among others. Furthermore, the implemented methods are illustrated with applications to real-life time series.     

Mutual information matrix based on Rényi entropy and application

Nonlinear Dynamics - Quantifying information dynamics in a nonlinear system is crucial in complex dynamics. The Mutual Information Matrix (MIM) method was developed to study nonlinear interactions...     

Information–Theoretic Aspects of Location Parameter Estimation under Skew–Normal Settings

In several applications, the assumption of normality is often violated in data with some level of skewness, so skewness affects the mean’s estimation. The class of skew–normal distributions is considered, given their flexibility for modeling data with asymmetry parameter. In this paper, we considered two location parameter (μ) estimation methods in the skew–normal setting, where the coefficient of variation and the skewness parameter are known. Specifically, the least square estimator (LSE) and the best unbiased estimator (BUE) for μ are considered. The properties for BUE (which dominates LSE) using classic theorems of information theory are explored, which provides a way to measure the uncertainty of location parameter estimations. Specifically, inequalities based on convexity property enable obtaining lower and upper bounds for differential entropy and Fisher information. Some simulations illustrate the behavior of differential entropy and Fisher information bounds.     

Properties and adsorptive capacity of amino acids modified chitosans for copper ion removal

This work presents the results of the modification of lateral groups of chitosan (2-amino-2-desoxy-β-D-glucose) by the reaction with different amino acids (glycine, L-lysine, -glutamic acid and L-isoleucine) under acid catalysis. The Cu²⁺ adsorption capacity of pure chitosan and of the chemically modified chitosans were also evaluated. The modification reaction favored the amide formation of the C-2 carbon of the glycoside ring under the adopted reaction conditions: reaction time and temperature and using sulfuric acid as a catalyst. The Cu²⁺ adsorption kinetics and equilibrium response using pure chitosan and the chemically modified chitosans as adsorbents showes that the adsorption capacity of equilibrium depended on the initial ion concentration. The response of each adsorbent gave good correlation with Langmuir's isotherm model. The following maximum adsorption capacity constants were obtained: 172.4 mg/g for chitosan and 69.9, 34.4, and 26.7 mg/g for modified chitosan with glycine, L-glutamic acid, and L-lysine, respectively. The adsorptive capacity seems to be dependent on the length and complexity of the added group.