Saeid Amiri

An Improvement of the Nonparametric Bootstrap Test for the Comparison of the Coefficient of Variations

In this article, we propose a new test for examining the equality of the coefficient of variation between two different populations. The proposed test is based on the nonparametric bootstrap method. It appears to yield several appreciable advantages over the current tests. The quick and easy implementation of the test can be considered as advantages of the proposed test. The test is examined by the Monte Carlo simulations, and also evaluated using various numerical studies.

Assessing the coefficient of variations of chemical data using bootstrap method

The coefficient of variation is frequently used in the comparison and precision of results with different scales. This work examines the comparison of the coefficient of variation without any assumptions about the underlying distribution. A family of tests based on the bootstrap method is proposed, and its properties are illustrated using Monte Carlo simulations. The proposed method is applied to chemical experiments with iid and non‐iid observations. Copyright

On the efficiency of bootstrap method into the analysis contingency table

The bootstrap method is a computer intensive statistical method that is widely used in performing nonparametric inference. Categorical data analysis, in particular the analysis of contingency tables, is commonly used in applied field. This work considers nonparametric bootstrap tests for the analysis of contingency tables. There are only a few research papers which exploit this field. The p-values of tests in contingency tables are discrete and should be uniformly distributed under the null hypothesis. The results of this article show that corresponding bootstrap versions work better than the standard tests. Properties of the proposed tests are illustrated and discussed using Monte Carlo simulations. This article concludes with an analytical example that examines the performance of the proposed tests and the confidence interval of the association coefficient.

Clustering Categorical Data via Ensembling Dissimilarity Matrices

ABSTRACT We present a technique for clustering categorical data by generating many dissimilarity matrices and combining them. We begin by demonstrating our technique on low-dimensional categorical data and comparing it to several other techniques that have been proposed. We show through simulations and examples that our method is both more accurate and more stable. Then we give conditions under which our method should yield good results in general. Our method extends to high-dimensional categorical data of equal lengths by ensembling over many choices of explanatory variables. In this context, we compare our method with two other methods. Finally, we extend our method to high-dimensional categorical data vectors of unequal length by using alignment techniques to equalize the lengths. We give an example to show that our method continues to provide useful results, in particular, providing a comparison with phylogenetic trees. Supplementary material for this article is available online.

Effect of region on the uncertainty in crop variety trial programs with a reduced number of trials

Results from crop variety trials may vary between geographical regions because of differences in climate and soil types. Results are usually presented at regional level. To evaluate the importance of the regions used in the Swedish variety trial programs, we examined which regions produced similar levels of yield and similar ratios in yield between cultivars; the amount by which variance could be reduced by division into regions or clusters of regions; and the amount of trials per region and year, replicates per trial, and trials per year required in order to fulfill specifications on the precision of results. Yield data from spring barley and winter wheat trials performed during 1997–2006 were studied using cluster analysis and variance component estimation. The objectives were (1) to discuss the effects of regions on precision when the number of trials has decreased; (2) to demonstrate the method; and (3) to report the results obtained. In spring barley, clusters of regions produced different levels of yield, but similar yield ratios between cultivars. In winter wheat, clusters of regions giving different yield ratios were identified. When the option of a single analysis was compared with that of region-wise analysis, the reduction in variance with the former, due to the larger number of trials, outweighed the reduction in variance with the latter due to decreased random interaction between trials and cultivars.

Randomly selected order statistics in ranked set sampling: A less expensive comparable alternative to simple random sampling

Rank-based sampling designs are powerful alternatives to simple random sampling (SRS) and often provide large improvements in the precision of estimators. In many environmental, ecological, agricultural, industrial and/or medical applications the interest lies in sampling designs that are cheaper than SRS and provide comparable estimates. In this paper, we propose a new variation of ranked set sampling (RSS) for estimating the population mean based on the random selection technique to measure a smaller number of observations than RSS design. We study the properties of the population mean estimator using the proposed design and provide conditions under which the mean estimator performs better than SRS and some existing rank-based sampling designs. Theoretical results are augmented with some numerical studies and a real-life example, where we also study the performance of our proposed design under perfect and imperfect ranking situations.

Comparison of genomic data via statistical distribution.

Sequence comparison has become an essential tool in bioinformatics, because highly homologous sequences usually imply significant functional or structural similarity. Traditional sequence analysis techniques are based on preprocessing and alignment, which facilitate measuring and quantitative characterization of genetic differences, variability and complexity. However, recent developments of next generation and whole genome sequencing technologies give rise to new challenges that are related to measuring similarity and capturing rearrangements of large segments contained in the genome. This work is devoted to illustrating different methods recently introduced for quantifying sequence distances and variability. Most of the alignment-free methods rely on counting words, which are small contiguous fragments of the genome. Our approach considers the locations of nucleotides in the sequences and relies more on appropriate statistical distributions. The results of this technique for comparing sequences, by extracting information and comparing matching fidelity and location regularization information, are very encouraging, specifically to classify mutation sequences.

Ranked set sampling with random subsamples

A ranked sampling procedure with random subsamples is proposed to estimate the population mean. Four methods of obtaining random subsamples are described. Several estimators of the mean of the population based on random subsamples in ranked set sampling are proposed. These estimators are compared with the mean of a simple random sample for estimating the mean of symmetric and skew distributions. Extensive simulation under several subsampling distributions, sample sizes, and symmetric and skew distributions shows that the estimators of the mean based on random subsamples are more accurate than existing methods.

The resampling of entropies with the application of biodiversity

This paper discusses the bootstrap test of entropies. Since the comparison of entropies is of prime interest in applied fields, finding an appropriate way to carry out such a comparison is of utmost importance. This paper presents how resampling should be performed to obtain an accurate p-value. Although the test using a pair-wise bootstrap confidence interval (CI) has already been dealt with in few works, here the bootstrap tests are studied because it may demand quite a different resampling algorithm compared with the CI. Moreover, the multiple test is studied. The proposed tests appear to yield several appreciable advantages. The easy implementation and the power of the proposed test can be considered as advantages. Here the entropy of the discrete variable is studied. The proposed tests are examined using Monte Carlo investigations and also evaluated using various distributions.

Comparison of tests of contingency tables

ABSTRACT We explore the use of bootstrap for testing independence of two categorical variables. We develop a theoretical justification for bootstrapping a contingency table and provide more accurate inference for small sample sizes. We also study the effect of equalized marginals on tests of independence. The small sample properties of the proposed and existing tests of independence are examined using Monte Carlo simulations. It is shown that the Fisher exact test and the Chi-squared test with continuity correction are very conservative and cannot be recommended to test independence with small sample sizes.