Differences in the activity of the muscles in the forearm of individuals with a congenital absence of the hand

The spectral content of the myoelectric signals from the muscles of the remnant forearms of three persons with congenital absences (CA) of their forearms was compared with signals from their intact contra-lateral limbs, similar muscles in three persons with acquired losses (AL) and seven persons without absences [no loss (NL)]. The observed bandwidth for the CA subjects was broader with peak energy between 200 and 300 Hz. While the signals from the contra-lateral limbs and the AL and NL subjects was in the 100-150 Hz range: The mean skew of the signals from the AL subjects was 46.3 +/- 6.7 and those with NL of 45.4 +/- 8.7, while the signals from those with CAs had a skew of 11.0 +/- 11. The structure of the muscles of one CA subject was observed ultrasonically. The muscle showed greater disruption than normally developed muscles. It is speculated that the myographic signal reflects the structure of the muscle. which has developed in a more disorganized manner as a result of the muscle not being stretched by other muscles across the missing distal joint, even in the muscles that are used regularly to control arm prostheses.

Position Errors Caused by GPS Height of Instrument Blunders

Height of instrument (HI) blunders in GPS measurements cause position errors. These errors can be pure vertical, pure horizontal, or a mixture of both. There are different error regimes depending on whether both the base and the rover both have HI blunders, if just the base has an HI blunder, or just the rover has an HI blunder. The resulting errors are on the order of 30 cm for receiver separations of 1000 km for an HI blunder of 2 m. Given the complicated nature of the errors, we believe it would be difficult, if not impossible, to detect such errors by visual inspection. This serves to underline the necessity to enter GPS HI's correctly.

Bayesian analysis for a skew extension of the multivariate null intercept measurement error model

Skew-normal distribution is a class of distributions that includes the normal distributions as a special case. In this paper, we explore the use of Markov Chain Monte Carlo (MCMC) methods to develop a Bayesian analysis in a multivariate, null intercept, measurement error model [R. Aoki, H. Bolfarine, J.A. Achcar, and D. Leao Pinto Jr, Bayesian analysis of a multivariate null intercept error-in -variables regression model, J. Biopharm. Stat. 13(4) (2003b), pp. 763-771] where the unobserved value of the covariate (latent variable) follows a skew-normal distribution. The results and methods are applied to a real dental clinical trial presented in [A. Hadgu and G. Koch, Application of generalized estimating equations to a dental randomized clinical trial, J. Biopharm. Stat. 9 (1999), pp. 161-178].

Inference for a skew extension of the Grubbs model

In this paper, we discuss inferential aspects for the Grubbs model when the unknown quantity x (latent response) follows a skew-normal distribution, extending early results given in Arellano-Valle et al. (J Multivar Anal 96:265-281, 2005b). Maximum likelihood parameter estimates are computed via the EM-algorithm. Wald and likelihood ratio type statistics are used for hypothesis testing and we explain the apparent failure of the Wald statistics in detecting skewness via the profile likelihood function. The results and methods developed in this paper are illustrated with a numerical example.

Multivariate measurement error models based on scale mixtures of the skew-normal distribution

Scale mixtures of the skew-normal (SMSN) distribution is a class of asymmetric thick-tailed distributions that includes the skew-normal (SN) distribution as a special case. The main advantage of these classes of distributions is that they are easy to simulate and have a nice hierarchical representation facilitating easy implementation of the expectation-maximization algorithm for the maximum-likelihood estimation. In this paper, we assume an SMSN distribution for the unobserved value of the covariates and a symmetric scale mixtures of the normal distribution for the error term of the model. This provides a robust alternative to parameter estimation in multivariate measurement error models. Specific distributions examined include univariate and multivariate versions of the SN, skew-t, skew-slash and skew-contaminated normal distributions. The results and methods are applied to a real data set.

Influence Diagnostics for a Skew Extension of the Grubbs Measurement Error Model

Influence diagnostics methods are extended in this article to the Grubbs model when the unknown quantity x (latent variable) follows a skew-normal distribution. Diagnostic measures are derived from the case-deletion approach and the local influence approach under several perturbation schemes. The observed information matrix to the postulated model and Delta matrices to the corresponding perturbed models are derived. Results obtained for one real data set are reported, illustrating the usefulness of the proposed methodology.

Reliability studies of the skew normal distribution

It has been observed in various practical applications that data do not conform to the normal distribution, which is symmetric with no skewness. The skew normal distribution proposed by Azzalini(1985) is appropriate for the analysis of data which is unimodal but exhibits some skewness. The skew normal distribution includes the normal distribution as a special case where the skewness parameter is zero. In this thesis, we study the structural properties of the skew normal distribution, with an emphasis on the reliability properties of the model. More specifically, we obtain the failure rate, the mean residual life function, and the reliability function of a skew normal random variable. We also compare it with the normal distribution with respect to certain stochastic orderings. Appropriate machinery is developed to obtain the reliability of a component when the strength and stress follow the skew normal distribution. Finally, IQ score data from Roberts (1988) is analyzed to illustrate the procedure.

The CLIMATE data base of monthly normals for globally gridded climatic variables (version 2.1)

This database (Leemans & Cramer 1991) contains monthly averages of mean temperature, temperature range, precipitation, rain days and sunshine hours for the terrestrial surface of the globe, gridded at 0.5 degree longitude/latitude resolution. All grd-files contain the same 62483 pixels in the same order, with 30' latitude and longitude resolution. The coordinates are in degree-decimals and indicate the SW corner of each pixel. Topography is from ETOPO5 and indicates modal elevation. Data were generated from a large data base, using the partial thin-plate splining algorithm (Hutchinson & Bischof 1983). This version is widely used around the globe, notably by all groups participating in the IGBP NPP model intercomparison.

An investigation of the effectiveness of existing bridge design methodology in providing adequate structural resistance to seismic disturbances : Phase III, Nonlinear soil-structure interaction of skew highway bridges : final report : prepared for Federal Highway Administration, Offices of Research and Development /

Bibliography: p. 46-48.

Climatography of the United States, no. 81-1-45. Decennial census of United States Climate - monthly normals of temperature, precipitation, and heating degree days.

Mode of access: Internet.