Journal of Biometrics & Biostatistics

The estimation of the gene frequency of sex-linked recessive traits is reconsidered. The estimation formulae for mixed, male, and female samples are presented and compared. Optimal designs for efficient estimation are studied. Male samples are optimal for gene frequencies below 1/3 and female samples for frequencies above 1/3. Mixed samples are never optimal. The model testing problem is discussed. Mixed samples are necessary for model testing. We analyse the loss in efficiency when both estimation and testing must be performed. In general, our results indicate that mixed samples should contain an excess of males. The results obtained are applied to empirical data found in the literature.

Background: For the data from the Los Angeles study in Breslow and Day of endometrial cancer and obtained from the 59 matched pairs using four dose levels of conjugated oestrogen, this study proposes new statistical models and gives an easy interpretation, as an approach to assess the data more properly. Methods: Proposing new statistical models for analyzing the endometrial cancer data, we apply them to the data, compare and assess the models considered here. Results: We have found a more preferable model which fits the data better than some existing models. Under the preferable model, we have seen that the average dose of oestrogen for case in a matched pair tends to be more than that for control in the pair. Conclusions: We have proposed two kinds of statistical models and made a conclusion that average dose for case tends to be more than that for control.

A CpG island is a short contiguous DNA subsequence that is rich in CG dinucleotides. CpG islands are often located around the promoters of housekeeping genes and have been found associated with certain tissue-specific genes. This observation indicates that they can be used as markers to identify genes. The information about the locations of CpG islands can also help us understand a gene regulation process called methylation. In this report, we propose a statistical method for locating CpG islands. Our method employs the Kullback-Leibler divergence. We use the given DNA sequence to determine a window size and a shift size for computing the divergence values along a DNA segment. A region in the proximity of a CpG island should contain consecutive windows with high divergence values. The distribution of the Kullback-Leibler divergence values can be suitably fitted by a truncated Pareto distribution. We estimate the parameters of the truncated Pareto distribution via the maximum likelihood principle. Then the fitted distribution is applied to locate regions with a divergence value exceeding a threshold level of significance. To assess the accuracy of our method, we compare our results to the putative CpG islands found in four well-studied mouse and human DNA sequences. The comparison suggests our approach consistently yields reliable predictions of CpG island locations.

Coefficient of variation has been widely used as a measure of precision of measurement equipment in particular for medical laboratories. The objective of this paper is to Inference for the coefficient of variation in uniform distributions. By using the two approaches of central limit theorem and generalized variable, confidence interval for coefficient of variation are considered. The coverage properties of the proposed confidence intervals for proposed two methods are assessment by simulation. An example using data of the approved metrological company from Iran Standard and Industrial Researches Organization is provided.