A statistical test vs. a validation experiment in Gene Expression Study

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Comparing two receptive vocabulary tests: The receptive one word vocabulary test and the peabody picture vocabulary test

This paper discusses a study to determine whether the Receptive One Word Picture Vocabulary Test is more useful than the Peabody Picture Vocabulary Test in assessing the vocabularies of hearing imparied children.

Reliability of one-repetition maximum test in untrained young adult men and women

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

One-repetition maximum test and isokinetic leg extension and flexion: correlations and predicted values

The most typical maximum tests for measuring leg muscle performance are the one-repetition maximum leg press test (1RMleg) and the isokinetic knee extension/flexion (IKEF) test. Nevertheless, their inter-correlations have not been well documented, mainly the predicted values of these evaluations. This correlational and regression analysis study involved 30 healthy young males aged 18-24y, who have performed both tests. Pearson's product moment correlation between 1RMleg and IKEF varied from 0.20 to 0.69 and the more exact predicted test was to 1RMleg (R2 = 0.71). The study showed correlations between 1RMleg and IKEF although these tests are different (isotonic vs. isokinetic) and provided further support for cross determination of 1RMleg and IKEF by linear and multiple linear regression analysis.

Power Calculations for Preclinical Studies Using a K-Sample Rank Test and the Lehmann Alternative Hypothesis

Power calculations in a small sample comparative study, with a continuous outcome measure, are typically undertaken using the asymptotic distribution of the test statistic. When the sample size is small, this asymptotic result can be a poor approximation. An alternative approach, using a rank based test statistic, is an exact power calculation. When the number of groups is greater than two, the number of calculations required to perform an exact power calculation is prohibitive. To reduce the computational burden, a Monte Carlo resampling procedure is used to approximate the exact power function of a k-sample rank test statistic under the family of Lehmann alternative hypotheses. The motivating example for this approach is the design of animal studies, where the number of animals per group is typically small.

Multinomial goodness-of-fit: large sample tests with survey design correction and exact tests for small samples

I introduce the new mgof command to compute distributional tests for discrete (categorical, multinomial) variables. The command supports largesample tests for complex survey designs and exact tests for small samples as well as classic large-sample x2-approximation tests based on Pearson’s X2, the likelihood ratio, or any other statistic from the power-divergence family (Cressie and Read, 1984, Journal of the Royal Statistical Society, Series B (Methodological) 46: 440–464). The complex survey correction is based on the approach by Rao and Scott (1981, Journal of the American Statistical Association 76: 221–230) and parallels the survey design correction used for independence tests in svy: tabulate. mgof computes the exact tests by using Monte Carlo methods or exhaustive enumeration. mgof also provides an exact one-sample Kolmogorov–Smirnov test for discrete data.

Face recognition robust to head pose from one sample image

Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. In 2004, we proposed a new face recognition algorithm, Adaptive Principal Component Analysis (APCA) [4], which performs well against both lighting variation and expression change. But like other eigenface-derived face recognition algorithms, APCA only performs well with frontal face images. The work presented in this paper is an extension of our previous work to also accommodate variations in head pose. Following the approach of Cootes et al, we develop a face model and a rotation model which can be used to interpret facial features and synthesize realistic frontal face images when given a single novel face image. We use a Viola-Jones based face detector to detect the face in real-time and thus solve the initialization problem for our Active Appearance Model search. Experiments show that our approach can achieve good recognition rates on face images across a wide range of head poses. Indeed recognition rates are improved by up to a factor of 5 compared to standard PCA.