An enhanced nonparametric quality control chart with application related to industrial process.

In various practical situations, the information about the process distribution is sometimes partially or completely unavailable. In these instances, practitioners prefer to use nonparametric charts as they don't restrict the assumption of normality or specific distribution. In this current article, a nonparametric double homogeneously weighted moving average control chart based on the Wilcoxon signed-rank statistic is developed for monitoring the location parameter of the process. The run-length profiles of the newly developed chart are obtained by using Monte Carlo simulations. Comparisons are made based on various performance metrics of run-length distribution among proposed and existing nonparametric counterparts charts. The extra quadratic loss is used to evaluate the overall performance of the proposed and existing charts. The newly developed scheme showed comparatively better results than its existing counterparts. For practical implementation of the suggested scheme, the real-world dataset related to the inside diameter of the automobile piston rings is also used.

Use of improved memory type control charts for monitoring cancer patients recovery time censored data.

Control charts are a statistical approach for monitoring cancer data that can assist discover patterns, trends, and unusual deviations in cancer-related data across time. To detect deviations from predicted patterns, control charts are extensively used in quality control and process management. Control charts may be used to track numerous parameters in cancer data, such as incidence rates, death rates, survival time, recovery time, and other related indicators. In this study, CDEC chart is proposed to monitor the cancer patients recovery time censored data. This paper presents a composite dual exponentially weighted moving average Cumulative sum (CDEC) control chart for monitoring cancer patients recovery time censored data. This approach seeks to detect changes in the mean recovery time of cancer patients which usually follows Weibull lifetimes. The results are calculated using type I censored data under known and estimated parameter conditions. We combine the conditional expected value (CEV) and conditional median (CM) approaches, which are extensively used in statistical analysis to determine the central tendency of a dataset, to create an efficient control chart. The suggested chart's performance is assessed using the average run length (ARL), which evaluates how efficiently the chart can detect a change in the process mean. The CDEC chart is compared to existing control charts. A simulation study and a real-world data set related to cancer patients recovery time censored data is used for results illustration. The proposed CDEC control chart is developed for the data monitoring when complete information about the patients are not available. So, instead of doping the patients information we can used the proposed chart to monitor the patients information even if it is censored. The authors conclude that the suggested CDEC chart is more efficient than competitor control charts for monitoring cancer patients recovery time censored data. Overall, this study introduces an efficient new approach for cancer patients recovery time censored data, which might have significant effect on quality control and process improvement across a wide range of healthcare and medical studies.

Efficient imputation methods in case of measurement errors.

This manuscript develops few efficient difference and ratio kinds of imputations to handle the situation of missing observations given that these observations are polluted by the measurement errors (ME). The mean square errors of the developed imputations are studied to the primary degree approximation by adopting Taylor series expansion. The proposed imputations are equated with the latest existing imputations presented in the literature. The execution of the proposed imputations is assessed by utilizing a broad empirical study utilizing some real and hypothetically created populations. Appropriate remarks are made for sampling respondents regarding practical applications.

A new unit distribution: properties, estimation, and regression analysis.

This research commences a unit statistical model named power new power function distribution, exhibiting a thorough analysis of its complementary properties. We investigate the advantages of the new model, and some fundamental distributional properties are derived. The study aims to improve insight and application by presenting quantitative and qualitative perceptions. To estimate the three unknown parameters of the model, we carefully examine various methods: the maximum likelihood, least squares, weighted least squares, Anderson-Darling, and Cramér-von Mises. Through a Monte Carlo simulation experiment, we quantitatively evaluate the effectiveness of these estimation methods, extending a robust evaluation framework. A unique part of this research lies in developing a novel regressive analysis based on the proposed distribution. The application of this analysis reveals new viewpoints and improves the benefit of the model in practical situations. As the emphasis of the study is primarily on practical applications, the viability of the proposed model is assessed through the analysis of real datasets sourced from diverse fields.

A new modified biased estimator for Zero inflated Poisson regression model.

Zero-inflated Poisson (ZIP) model is widely used for counting data with excessive zeroes. The multicollinearity is the common factor in the explanatory variables of the count data. In this context, typically, maximum likelihood estimation (MLE) generates unsatisfactory results due to inflation of mean square error (MSE). In the solution of this problem usually, ridge parameters are used. In this study, we proposed a new modified zero-inflated Poisson ridge regression model to reduce the problem of multicollinearity. We experimented within the context of a specified simulation strategy and recorded the behavior of proposed estimators. We also apply our proposed estimator to the real-life data set and explore how our proposed estimators perform well in the presence of multicollinearity with the help of ZIP model for count data.

A novel extension of half-logistic distribution with statistical inference, estimation and applications.

In the present study, we develop and investigate the odd Frechet Half-Logistic (OFHL) distribution that was developed by incorporating the half-logistic and odd Frechet-G family. The OFHL model has very adaptable probability functions: decreasing, increasing, bathtub and inverted U shapes are shown for the hazard rate functions, illustrating the model's capacity for flexibility. A comprehensive account of the mathematical and statistical properties of the proposed model is presented. In estimation viewpoint, six distinct estimation methodologies are used to estimate the unknown parameters of the OFHL model. Furthermore, an extensive Monte Carlo simulation analysis is used to evaluate the effectiveness of these estimators. Finally, two applications to real data are used to demonstrate the versatility of the suggested method, and the comparison is made with the half-logistic and some of its well-known extensions. The actual implementation shows that the suggested model performs better than competing models.

Design based synthetic imputation methods for domain mean.

In real life, situations may arise when the available data are insufficient to provide accurate estimates for the domain, the small area estimation (SAE) technique has been used to get accurate estimates for the variable under study. The problem of missing data is a serious problem that has an impact on sample surveys, but small area estimates are especially prone to it. This paper is a basic effort that suggests design based synthetic imputation methods for the domain mean estimation using simple random sampling in order to address the issue of missing data under SAE. The expression of the mean square error for the proposed imputation methods are obtained up to first order approximation. The efficiency conditions are determined and a thorough simulation study is carried out using artificially generated data sets. An application is included with real data that further supports this study.

Statistical modelling for a new family of generalized distributions with real data applications.

The modern trend in distribution theory is to propose hybrid generators and generalized families using existing algebraic generators along with some trigonometric functions to offer unique, more flexible, more efficient, and highly productive G-distributions to deal with new data sets emerging in different fields of applied research. This article aims to originate an odd sine generator of distributions and construct a new G-family called "The Odd Lomax Trigonometric Generalized Family of Distributions". The new densities, useful functions, and significant characteristics are thoroughly determined. Several specific models are also presented, along with graphical analysis and detailed description. A new distribution, "The Lomax cosecant Weibull" (LocscW), is studied in detail. The versatility, robustness, and competency of the LocscW model are confirmed by applications on hydrological and survival data sets. The skewness and kurtosis present in this model are explained using modern graphical methods, while the estimation and statistical inference are explored using many estimation approaches.

Modeling Various Survival Distributions using a Nonparametric Hypothesis Testing Based on Laplace Transform Approach with Some Real Applications.

We hope to assess the different processing methodologies or the effectiveness of the devices or systems applied in this nonparametric statistical test by observing the failure behavior of the recorded survival data. The proposed second-order approach of new better than used (NBU 2) will be employed, which requires that the test data behaves either like NBU 2 property or exponentially. If the survival data is NBU 2, the proposed treatment method is likely to be beneficial. If the data are exponential, on the other hand, the recommended treatment method has no positive or negative influence on patients, as shown in the application section. To establish the validity of the test, we calculated the power of the proposed test and efficiency on both complete and censored data, compared the results to those of existing tests, and then applied the test to a range of real-world data.

Statistical Modeling of Some Cancerous Diseases Using the Laplace Transform Approach of Basic Life Testing Issues.

The purpose of the nonparametric statistical test used in this study is to compare different treatment options by looking at failure behavior in recorded survival data. Patients' survival times are documented after using the proposed approach. The observed data's behavior was assumed to be based on used better than aged in the moment generating function order (UBAmgf) characteristic or a constant failure rate in this study (exponential scenario). Suppose that the survival data is UBAmgf, then the treatment or the machine or system in use produces a better or a higher expected total present value than an older machine governed by an exponential survival function; if the data is exponential, the suggested treatment strategy is ineffective (the recommended treatment approach has neither positive or negative effects on the patients). To guarantee that the suggested statistical test is used correctly, the efficiency and critical values are calculated and compared to those of other tests, and the technique is then applied to medical data.

Sero diagnosis of hydatid disease.

Two serological test, double agar gel diffusion test (DD5T) and indirect haemagglutination test (IHAT) were performed for diagnosis of hydatid disease. Correlation of their results with eosinophilia were also made. DD5T gave higher sensitivity (100%) than IHAT which gave 95%. Out of the serologically positive patients, only 50% had eosinophilia. Sheep hydatid antigen was more potent than camel antigen. The supernatant lyophilized sheep antigen was the best antigen used.

Flea ectoparasites of commensal rodents in Shebin El Kom, Menoufia Governorate, Egypt.

Rodents population are widely distributed in some Egyptian Governorates, particularly those in the Nile Delta. Generally speaking, rodents particularly those living in close association with man, play a major role in human health, welfare and economy. Besides, their arthropod ectoparasites are important vectors of pathogenic agents as well as they themselves are causative agents of many allergic disorders. No doubt, the increase in rodent population is followed by an increase in many zoonotic diseases. In Shebin El Kom city, the capital of Menoufia Governorate three commensal rodents were identified. They were Mus musculus (house mouse), Rattus rattus (black rat) and Rattus norvegicus (Norway rat). The maximal rat index was in Autumn (0.69) and minimal was in Winter (0.2). Five species of fleas were collected from the rodents. They were Xenopsylla cheopis (57.7%), Xenopsylla ramesis. (12.7%), Pulex irritans (12.7%), Leptopsylla segnis (11.3%) and Ctenocephalides felis (5.6%). The maximal flea index was in Spring (1.02) and the minimal was in Winter (zero) The highly infested rodent species was R. rattus (0.65/rat), R. norvegicus (0.62/rat) and the lowest was M. musculus (0.45/mouse). The overall flea index was 0.59. The role of fleas in disease transmission was included. No doubt, to carry out feasible control measures against rodents and their ectoparasites complete understanding of their bionomics must be well studied.

Mites infesting commensal rodents in Shebin El Kom, Menoufia G., Egypt.

Rodent populations markedly increased in many Egyptian Governorates particularly commensal ones which live in close association with man and his domestic animals. In this paper, commensal rodents; M. musculus R. r. alexandrinus, and R. norvegicus were examined for the mites infesting them. Four species of mites were recovered; Ornithonyssus bacoti; Laelaps nuttalli, Dermanyssus gallinae and Allodermanyssus sanguineus. The medical and economic importance was discussed. It was concluded that mites and their environmental problem should be tackled.

Human scabies acquired from a pet puppy.

Scabies is a very old contagious skin disease caused by itch mite, Sarcoptes scabiei which burrows into the stratum corneum. Nowadays, human scabies is one of the health problems among the school children. This paper dealt with a case of scabies in a preschool aged child who acquired the infestation from his pet puppy.

Effect of toxoplasmosis on experimental schistosomiasis.

One hundred and twenty Swiss albino mice were infected with avirulent strain of Toxoplasma gondii (Czechoslovakian strain) challenged with Schistosoma mansoni at different intervals of one week, one month, and two months. Worm load, egg count, oogram pattern and liver and spleen weight were assessed for each mouse. Toxoplasmosis induced a significant resistance to Schistosoma mansoni challenge infection as evidenced by the reduction in worm load, egg count and changes in oogram pattern. The mean liver weights of concomitantly infected mice was increased which was not significantly different from their corresponding Schistosoma infected controls but the increase in spleen weight was significant.

Immunological and histopathological studies on the effect of toxoplasmosis in experimental schistosomiasis.

One hundred and twenty Swiss albino mice were infected with avirulent strain of Toxoplasma gondii. Then challenged with Schistosoma mansoni at different time intervals of one week, one month and two months. The immunological and histopathological changes were assessed for each mouse. The results showed that toxoplasmosis caused specific humoral and cellular immunosuppression to Schistosoma mansoni infection.