During the past couple of years, statistical distributions have been widely used in applied areas such as reliability engineering, medical, and financial sciences. In this context, we come across a diverse range of statistical distributions for modeling heavy tailed data sets. Well-known distributions are log-normal, log-t, various versions of Pareto, log-logistic, Weibull, gamma, exponential, Rayleigh and its variants, and generalized beta of the second kind distributions, among others. In this paper, we try to supplement the distribution theory literature by incorporating a new model, called a new extended Weibull distribution. The proposed distribution is very flexible and exhibits desirable properties. Maximum likelihood estimators of the model parameters are obtained, and a Monte Carlo simulation study is conducted to assess the behavior of these estimators. Finally, we provide a comparative study of the newly proposed and some other existing methods via analyzing three real data sets from different disciplines such as reliability engineering, medical, and financial sciences. It has been observed that the proposed method outclasses well-known distributions on the basis of model selection criteria.
In this paper, we proposed two new families of estimators using the supplementary information on the auxiliary variable and exponential function for the population distribution functions in case of nonresponse under simple random sampling. The estimations are done in two nonresponse scenarios. These are nonresponse on study variable and nonresponse on both study and auxiliary variables. As we have highlighted above that two new families of estimators are proposed, in the first family, the mean was used, while in the second family, ranks were used as auxiliary variables. Expression of biases and mean squared error of the proposed and existing estimators are obtained up to the first order of approximation. The performances of the proposed and existing estimators are compared theoretically. On these theoretical comparisons, we demonstrate that the proposed families of estimators are better in performance than the existing estimators available in the literature, under the obtained conditions. Furthermore, these theoretical findings are braced numerically by an empirical study offering the proposed relative efficiencies of the proposed families of estimators.
This study attempts to test the effect of different social-economic factors on the birth weight of newly born in rural and urban areas of the province Punjab, Pakistan, using Bivariate and multiple linear regression analysis. The present study is based on the date of PHDS (2006-07). The total sample size is 3705. The effect of type of residence, respondent's month of birth, sex of the child, respondent, age at first birth, preceding the interval and succeeding interval, educational level of respondent, and birth order number has been explored for birth weight of newly born children. The type of residence, Respondent's month of birth, Respondent's age at first birth, and birth order number have a significant effect on the birth weight of newly born but the sex of the child, respondents of education level. And succeeding and preceding birth interval have an insignificant effect on birth weight of newly born. It is recommended that Government should take a step to provide better education; more institutions should be open in rural areas, because as people become educated their behavior towards the low birth weight of newly born.
Introduction: Fractures are a leading cause of emergency presentation in pediatric age group and comprise 9% cause of healthcare presentation in children. About 1/3rd fractures in young age occur before 7 years of age 3, but are particularly prevalent in ages between 10-14 years and especially involve the male gender. Growing bone has the remarkable tendency of being able to correct underlying displacement as well as the risk of growth disturbance.
Patients and Methods: A Cross-sectional study was carried at Benazir Bhutto Hospital and Holy Family Hospital, Rawalpindi, which are affiliated with Rawalpindi Medical University. All children older than 6 months and younger than fourteen years of age presenting between January 2017 and June 2017 were included in the study. All concerned data was collected on a predesigned questionnaire.
Results: Total two hundred pediatric patients were enrolled in the study, out of which 139 (69.5%) were males and 61 (30.5%) were females, with a mean age of 8.9 ± 2.7 years. The most vulnerable group was between 6 to 10 years and the most common cause was fall. 97.5% of patients had closed fractures and only 2.5% had open fractures. 143 (71.5%) were from the urban area and only 57 (28.5%) were from rural areas.
Conclusion: Long bone fractures in children are very common. Early detection and timely management of these fractures is the key to better outcomes and preventing disabilities in young people.
<abstract><p>Estimators for the finite population mean of the research variable are proposed in this article, employing ratio, product, and regression type estimators, all of which need just one auxiliary variable. A first-order approximation is developed for the mean squared errors of the techniques provided. It has been proven theoretically that the suggested estimators perform better than current estimators, and these theoretical conditions have been validated numerically using four data sets.</p></abstract>
In this article, a new approach is used to introduce an additional parameter to a continuous class of distributions. The new class is referred to as a new extended-F family of distributions. The new extended-Weibull distribution, as a special submodel of this family, is discussed. General expressions for some mathematical properties of the proposed family are derived, and maximum likelihood estimators of the model parameters are obtained. Furthermore, a simulation study is provided to evaluate the validity of the maximum likelihood estimators. Finally, the flexibility of the proposed method is illustrated via two applications to real data, and the comparison is made with the Weibull and some of its well-known extensions such as Marshall–Olkin Weibull, alpha power-transformed Weibull, and Kumaraswamy Weibull distributions.
Extensive pumping and less recharge of groundwater increase concentration of Arsenicin it; thus, make it injurious for human health. This study explores the water quality indicators of groundwater of the arid and populous areas of Southern Punjab – Pakistan, which experiences extensive pumping for irrigation and drinking purposes. Spatial data of 550 locations from the three major cities of this area namely Multan, Muzaffargarh, and Khanewal were collected to observe the 8 well known water quality indicators set by Pakistan Environmental Protection Agency (PEPA) and World Health Organization (WHO). Firstly, we computed pairwise correlation coefficients among these indicators and later on contamination load for this data was also calculated by using factor analysis. Several regression models were fitted and the established equations were evaluated based on their coefficient of determination for pairs of variables. Factor analysis suggested 3 groups of indicators that were chemically correlated and showed concurrence with regression analysis. Among the water samples, 27% showed undesired Arsenic concentration that violated thePEPA and WHO standards. Lastly, we spatially mapped these indicators to predict the unmonitored locations which will enable stakeholders and policymakers to take decisions in the best interest of the health of community living in these areas.
Objectives: The current study is conducted to compare serum 25-hydroxyvitamin D concentrations and total testosterone in young, physically fit adult males. Study Design: the study was based on cross sectional design. Study Settings: The research was conducted in Department of Pathology, Shahida Islam Medical and Dental College situated in District Lodhran between 1st July, 2023 and December, 2023. Methods: This cross-sectional research, which took place at the Department of Pathology at Shahida Islam Medical and Dental College in Lodhran from July 1, 2023, to December 31, 2023, had 176 young male volunteers, all between the ages of 18 and 35. Each participant was given questionnaires to fill out on their prior medical history, food habits, and rigorous exercise levels. Every participant had their blood drawn following a 12-hour fast and a 24-hour period without physical exercise. Separating serum was done with a centrifuge. The blood concentration of LH, FSH, total testosterone, and 25-hydroxyvitamin D were measured by electrochemiluminescence (ECLIA). Results: A total of 176 individuals in good health were registered, with a mean age of 26.62±5.20 years. The presence of vitamin D of less than 10 ng/ml, between 10–20 ng/ml, and greater than 20 ng/ml were reported in 70 (39.8%), 76 (43.2%), and 30 (17.0%) individuals, accordingly. The mean hormone levels (TT, FSH, and LH) in the three 25(OH)D groups did not vary statistically. Our findings showed that there existed no statistically substantial relationship in the categories under study between 25(OH)D and LH, FSH, and TT. Conclusion: We found little variation in the condition of the 25(OH)D concentration and the average hormonal measurements (LH, FSH, and TT). Based on these data, we concluded that in young, healthy guys, there is no relationship between testosterone concentrations and deficient or inadequate 25(OH)D level.
The use of extreme values of the auxiliary variable is sometimes more beneficial to get the high efficiency of the estimators, and the study variable can have a correlation with the rank of the decently correlated auxiliary variable. As a result, it can be regarded as additional data for the study variable that can be used to improve the estimators’ efficiency. When the knowledge of the minimum and maximum values, as well as the rankings of the auxiliary variable, is known, various better estimators for calculating the finite population mean of the research variable based on extreme values under simple random sampling are proposed in this paper. The suggested estimators’ bias and mean squared error expressions are derived using first-order approximation. The recommended estimators have been compared mathematically to the current estimators. The suggested estimators are more exact in terms of relative efficiency than the other estimators addressed here, as shown by simulation and real datasets used to demonstrate the estimation of a limited population mean based on extreme values.
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