Robust Statistical Inference in Generalized Linear Models Based on Minimum Renyi’s Pseudodistance Estimators

Minimum Renyi’s pseudodistance estimators (MRPEs) enjoy good robustness properties without a significant loss of efficiency in general statistical models, and, in particular, for linear regression models (LRMs). In this line, Castilla et al. considered robust Wald-type test statistics in LRMs based on these MRPEs. In this paper, we extend the theory of MRPEs to Generalized Linear Models (GLMs) using independent and nonidentically distributed observations (INIDO). We derive asymptotic properties of the proposed estimators and analyze their influence function to asses their robustness properties. Additionally, we define robust Wald-type test statistics for testing linear hypothesis and theoretically study their asymptotic distribution, as well as their influence function. The performance of the proposed MRPEs and Wald-type test statistics are empirically examined for the Poisson Regression models through a simulation study, focusing on their robustness properties. We finally test the proposed methods in a real dataset related to the treatment of epilepsy, illustrating the superior performance of the robust MRPEs as well as Wald-type tests.

AWARENESS OF ANEMIA AMONG RURAL PREGNANT WOMEN IN BAGERHAT DISTRICT OF BANGLADESH

Background: Anemia in pregnancyis a decrease in the total red blood cells (RBCs) or hemoglobin in the blood duringpregnancyor in the period following pregnancy. It is the condition of having a lower-than-normal number of red blood cells or quantity of hemoglobin. Anemia diminishes the capacity of the blood to carry oxygen. Patients with anemia may feel tired, fatigue easily, appear pale, develop palpitations, and become shortness of breath. Objectives: The purpose of the present study was to investigate about the awareness of anemia among rural pregnant women in Bagerhat district of Bangladesh Materials and Method: A sample of 29 pregnant women (PW) from a total of listed 111 women from three upazilas of Bagerhat district was considered for assessing the awareness of Anemia. Those three upazilas were taken at random first from the nine upazilas of Bagerhat district.Data on different variables were collected directly from the selected women through a prescribed questionnaire. Descriptive statistics e.g., maximum, minimum, mean, standard deviation, skewness, kurtosis, etc. of the variables together with their standard error of their estimates were considered foranalyzing sample characteristics of the study. The relationship between two nominal variables is assessed by cross tabulation with test statistics Phi and Cramers V. The bootstrapresampling method was used to understand the population parameters. Results: About86% pregnant women have no idea about anemia and also their causes. All respondents feel weakness that indicates they have the symptom of anemia. The phi andcramersV imply that the relationship between heard about anemia and the source of information is highly significant (p= 0.000). Among the awarded women in Bagerhat district, probability of getting awareness from service provider was0.917 and that from relatives was 0.083. Among the population, the probability of contribution of the service provider was0.379. Probability of unknown was 0.586 indicating much populationin Bagerhat district werenot aware about anemia. Probability of getting information of anemia from mother was zero indicating very recently service providers have started their program in Bagerhat district. Conclusion: Probability of getting information of anemia from mother is zero indicating very recently service providers have started their program in Bagerhat district. Therefore, this program should be continued until the probability of getting information from mother will be closed to 1. Recommendation: Government as well as the NGOs should continue & enhance the present awareness program in Bagerhat district.

Gene Expression in Proliferative Diabetic Retinopathy Using RNA-Seq Data: A Computational Study on Saudi Patients

Proliferative diabetic retinopathy is the widespread type of DM which causes chronic as well as progressive alterations at microvascular level, which particularly effects the eye. The main characteristic of this disease is the development of few new blood vessels around the retina of eye as well as at the posterior region of eye segments. For our computational analysis 155 differentially expressed genes calculated through paired t test statistics analysis using the GenePattern platform, of proliferative diabetic retinopathy in Saudi patients were downloaded. Among the 155 genes, 95 were upregulated, and 60 were downregulated. The Annotation Cluster (FAC) tool in the (DAVID) (http://david.abcc.ncifcrf.gov/home.jsp) was used to identify biological processes that are abundant in proliferative diabetic retinopathy (PDR). The functions required for response to mRNA splicing, intracellular protein transport, mRNA processing, microtubule cytoskeleton structure, and atrioventricular canal formation are represented by the GO keywords that are abundant in genes. We used the KAAS web server to identify the biological pathways of these DEGs in addition to DAVID functional analysis and found that the majority of the DEGs were associated with important biological processes, with many being classified in metabolic pathways, Spliceosome, Cell cycle, or being involved in the mRNA surveillance pathway. findings are consistent with those of earlier research. To corroborate the predictions stated in this work, which will demonstrate the role enhanced functional processes, experimental validation will be necessary.

A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables

The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since statistical power is often limited, the specification of environmental effects is nontrivial, and such misspecifications can lead to false positive findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy increases power to detect interactions, identifying contributing key genes and pathways is difficult based on these global results.Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate multiple genetic variants and/or multiple environmental factors. Using sample splitting, a screening step enables the selection and combination of potential interactions into scores with improved interpretability, based on the user’s unrestricted choices for statistical/machine learning approaches. In the testing step, the application of robust test statistics minimizes the susceptibility of the results to main effect misspecifications.Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified genome-wide significant interactions with subcomponents of genetic risk scores. While the contributing single variant interactions are moderate, our analysis results indicate interesting interaction patterns that result in strong aggregated signals that provide further insights into gene-environment interaction mechanisms.

Statistical reproducibility for pairwise t-tests in pharmaceutical research

This paper investigates statistical reproducibility of the [Formula: see text]-test. We formulate reproducibility as a predictive inference problem and apply the nonparametric predictive inference method. Within our research framework, statistical reproducibility provides inference on the probability that the same test outcome would be reached, if the test were repeated under identical conditions. We present an nonparametric predictive inference algorithm to calculate the reproducibility of the [Formula: see text]-test and then use simulations to explore the reproducibility both under the null and alternative hypotheses. We then apply nonparametric predictive inference reproducibility to a real-life scenario of a preclinical experiment, which involves multiple pairwise comparisons of test groups, where different groups are given a different concentration of a drug. The aim of the experiment is to decide the concentration of the drug which is most effective. In both simulations and the application scenario, we study the relationship between reproducibility and two test statistics, the Cohen’s [Formula: see text] and the [Formula: see text]-value. We also compare the reproducibility of the [Formula: see text]-test with the reproducibility of the Wilcoxon Mann–Whitney test. Finally, we examine reproducibility for the final decision of choosing a particular dose in the multiple pairwise comparisons scenario. This paper presents advances on the topic of test reproducibility with relevance for tests used in pharmaceutical research.

A test for fuzzy exponentiality based on Kullback-Leibler information

The exponential distribution has been widely used in engineering, social and biological sciences. In this paper, we propose a new goodness-of-fit test for fuzzy exponentiality using α-pessimistic value. The test statistics is established based on Kullback-Leibler information. By using Monte Carlo method, we obtain the empirical critical points of the test statistic at four different significant levels. To evaluate the performance of the proposed test, we compare it with four commonly used tests through some simulations. Experimental studies show that the proposed test has higher power than other tests in most cases. In particular, for the uniform and linear failure rate alternatives, our method has the best performance. A real data example is investigated to show the application of our test.

Detection Performance Analysis of the Standard FDA and FDA-MIMO Radar in the Gaussian Background

This paper studies the optimal detection performance of the standard frequency diverse array (FDA) radar and FDA multi-input multioutput (FDA-MIMO) radar in Gaussian clutter and noise. Array signal processing scheme at the receiver is firstly designed to obtain the array steering vector containing range, azimuth, and frequency increment. For the two array configurations, namely, collocated transmit-receive and collocated transmit distributed receive, the likelihood ratio test statistics and the test statistic distributions are derived in the Neyman–Pearson sense. It is then investigated how the number of array elements influences the detection performance of various radar systems at low signal-to-noise ratio (SNR). Several numerical simulations are carried out to demonstrate that the performance improvement is hard for MIMO and FDA-MIMO by only increasing the number of transmit elements, while it is achievable for the FDA. The paper finally makes a comparative analysis for detection performances of five radar configurations under different SNRs.

A computationally efficient clustering linear combination approach to jointly analyze multiple phenotypes for GWAS

There has been an increasing interest in joint analysis of multiple phenotypes in genome-wide association studies (GWAS) because jointly analyzing multiple phenotypes may increase statistical power to detect genetic variants associated with complex diseases or traits. Recently, many statistical methods have been developed for joint analysis of multiple phenotypes in genetic association studies, including the Clustering Linear Combination (CLC) method. The CLC method works particularly well with phenotypes that have natural groupings, but due to the unknown number of clusters for a given data, the final test statistic of CLC method is the minimum p-value among all p-values of the CLC test statistics obtained from each possible number of clusters. Therefore, a simulation procedure must be used to evaluate the p-value of the final test statistic. This makes the CLC method computationally demanding. We develop a new method called computationally efficient CLC (ceCLC) to test the association between multiple phenotypes and a genetic variant. Instead of using the minimum p-value as the test statistic in the CLC method, ceCLC uses the Cauchy combination test to combine all p-values of the CLC test statistics obtained from each possible number of clusters. The test statistic of ceCLC approximately follows a standard Cauchy distribution, so the p-value can be obtained from the cumulative density function without the need for the simulation procedure. Through extensive simulation studies and application on the COPDGene data, the results demonstrate that the type I error rates of ceCLC are effectively controlled in different simulation settings and ceCLC either outperforms all other methods or has statistical power that is very close to the most powerful method with which it has been compared.

Community Policing and COVID-19 Safety Precautions: An Explanatory Study of Agbopa in Abeokuta, Ogun State, Nigeria

Community policing means synergy between members of the public and police institutions to fight crimes. This could be deployed to address coronavirus pandemic that is currently threatening global peace and security. There are a number of safety precautions that are put in place to stem the spread of the deadly virus. One of the challenges of these measures is poor compliance. This leads to contact with the disease thereby putting pressure on scanty health facilities, crisis at the family level due to loss of breadwinners and loved ones. Hence, this paper assesses the role of community policing in enforcing COVID-19 safety precautions in a bid to mitigate the health emergency. The study adopts mixed research methods; data were sourced secondarily through the content analysis of peer-reviewed journals, edited text-books and online resources. The primary data were gotten by administering questionnaires on 364 respondents. Taro Yamane formula was used in arriving the sample size from the total population of 4,000 people. Frequency counts and correlation were used in primary data analysis. The two hypotheses tested led to rejection of null hypotheses and acceptance of alternative hypothesis. The test statistics are (P= 0.000, R= 0.144, 5%), (P=0.00. R= 0.098, 5%). Findings revealed that community policing approach significantly enhanced wearing of face masks, social distance in the schools, worship centres and during transportation. The study recommends effective collaborations among people at the grassroots to end COVID-19 pandemic. The paper will be useful to individuals, Community Development Associations, National Centre for Disease Control and other stakeholders.