Nonlinear prediction based on score function

The linear prediction coding of speech is based in the assumption that the generation model is autoregresive. In this paper we propose a structure to cope with the nonlinear effects presents in the generation of the speech signal. This structure will consist of two stages, the first one will be a classical linear prediction filter, and the second one will model the residual signal by means of two nonlinearities between a linear filter. The coefficients of this filter are computed by means of a gradient search on the score function. This is done in order to deal with the fact that the probability distribution of the residual signal still is not gaussian. This fact is taken into account when the coefficients are computed by a ML estimate. The algorithm based on the minimization of a high-order statistics criterion, uses on-line estimation of the residue statistics and is based on blind deconvolution of Wiener systems [1]. Improvements in the experimental results with speech signals emphasize on the interest of this approach.

Regression-based techniques for statistical decision making in single-case designs

The present study evaluates the performance of four methods for estimating regression coefficients used to make statistical decisions regarding intervention effectiveness in single-case designs. Ordinary least squares estimation is compared to two correction techniques dealing with general trend and one eliminating autocorrelation whenever it is present. Type I error rates and statistical power are studied for experimental conditions defined by the presence or absence of treatment effect (change in level or in slope), general trend, and serial dependence. The results show that empirical Type I error rates do not approximate the nominal ones in presence of autocorrelation or general trend when ordinary and generalized least squares are applied. The techniques controlling trend show lower false alarm rates, but prove to be insufficiently sensitive to existing treatment effects. Consequently, the use of the statistical significance of the regression coefficients for detecting treatment effects is not recommended for short data series.

A comparison of three established age estimation methods on an adult Spanish sample.

Most current methods for adult skeletal age-at-death estimation are based on American samples comprising individuals of European and African ancestry. Our limited understanding of population variability hampers our efforts to apply these techniques to various skeletal populations around the world, especially in global forensic contexts. Further, documented skeletal samples are rare, limiting our ability to test our techniques. The objective of this paper is to test three pelvic macroscopic methods (1-Suchey-Brooks; 2- Lovejoy; 3- Buckberry and Chamberlain) on a documented modern Spanish sample. These methods were selected because they are popular among Spanish anthropologists and because they never have been tested in a Spanish sample. The study sample consists of 80 individuals (55 ♂ and 25 ♀) of known sex and age from the Valladolid collection. Results indicate that in all three methods, levels of bias and inaccuracy increase with age. The Lovejoy method performs poorly (27%) compared with Suchey-Brooks (71%) and Buckberry and Chamberlain (86%). However, the levels of correlation between phases and chronological ages are low and comparable in the three methods (< 0.395). The apparent accuracy of the Suchey-Brooks and Buckberry and Chamberlain methods is largely based on the broad width of the methods" estimated intervals. This study suggests that before systematic application of these three methodologies in Spanish populations, further statistical modeling and research into the co-variance of chronological age with morphological change is necessary. Future methods should be developed specific to various world populations, and should allow for both precision and flexibility in age estimation.

A Collation of Recently published Western European Formulae for Age Estimation of Subadult Skeletal Remains: Recommendations For Forensic Anthropology and Osteoarchaeology

The aim of this study is to provide an effective and quick reference guide based on the most useful European formulae recently published for subadult age estimation. All of these formulae derive from studies on postnatal growth of the scapula, innominate, femur, and tibia, based on modern skeletal data (173 ♂, 173 ♀) from five documented collections from Spain, Portugal, and Britain. The formulae were calculated from Inverse Regression. For this reason, these formulae are especially useful for modern samples from Western Europe and in particular on 20th century human remains from the Iberian Peninsula. Eleven formulae were selected as the most useful because they can be applied to individuals from within a wide age range and in individuals of unknown sex. Due to their high reliability and because they derive from documented European skeletal samples, we recommend these formulae be used on individuals of Caucasoid ancestry from Western Europe.

Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

This paper proposes a pose-based algorithm to solve the full SLAM problem for an autonomous underwater vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a mechanical scanning imaging sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method utilizes two extended Kalman filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach

A correlation sensitivity analysis of non-life underwriting risk in solvency capital requirement estimation

This paper analyses the impact of using different correlation assumptions between lines of business when estimating the risk-based capital reserve, the Solvency Capital Requirement -SCR-, under Solvency II regulations. A case study is presented and the SCR is calculated according to the Standard Model approach. Alternatively, the requirement is then calculated using an Internal Model based on a Monte Carlo simulation of the net underwriting result at a one-year horizon, with copulas being used to model the dependence between lines of business. To address the impact of these model assumptions on the SCR we conduct a sensitivity analysis. We examine changes in the correlation matrix between lines of business and address the choice of copulas. Drawing on aggregate historical data from the Spanish non-life insurance market between 2000 and 2009, we conclude that modifications of the correlation and dependence assumptions have a significant impact on SCR estimation.