In this paper, we consider the Bayesian estimation of the parameters and reliability function for a Cosine inverse log compound Rayleigh distribution under squared error and squared logarithmic loss functions. We use Lindley’s approximation to compute the Bayesian estimates. This method is evaluated using mean square error through simulation study with varying sample size.

This study presents a descriptive statistical analysis of a retrospective study on Polycystic Ovary Syndrome (PCOS), a common endocrine disorder among women of reproductive age that can lead to various metabolic, hormonal, and reproductive complications. Data were collected from the Misurata Infertility Center between 2023 and 2024 from 250 women undergoing infertility evaluation. Demographic data, including age and body mass index (BMI), along with medical records, were reviewed. Hormonal parameters assessed were Luteinizing Hormone (LH), Follicle-Stimulating Hormone (FSH), Estradiol, Prolactin, Anti-Müllerian Hormone (AMH), and Thyroid-Stimulating Hormone (TSH). Biochemical markers included blood sugar (BS), HbA1c, and Vitamin D levels. Results indicated that the average BMI was 28.72, which is above the World Health Organization's ideal range (18.5–24.9), suggesting that most patients were overweight. The mean Estradiol level was 47.95 pg/mL (within the ideal range of 0–80), and the mean FSH was 7.704 mIU/mL (within the ideal range of 1.37–9.9). In contrast, the mean Prolactin level was 23.148 ng/mL, exceeding the ideal range (4.5–21.5), indicating hyperprolactinemia in most patients. The mean Vitamin D level was 21.22 ng/mL, below the ideal range (30–50), indicating a deficiency. The mean BS was 105.92 mg/dL (within normal range: 70–110), and the average HbA1c was 5.622%, suggesting most patients were not diabetic. Keywords: Polycystic Ovary Syndrome; Demographic Data; Medical Records; Hormonal Levels; Misurata Infertility Center.

In the current study a new family of distributions called the odd Lindley Log compound Rayleigh distribution is introduced and studied. Some statistical properties of the new distribution including moments, moment generating function and Renyi entropy are derived. Parameter estimations are obtained via the maximum likelihood method and the observed information matrix is derived. Finally, a real dataset is used to illustrate the importance and flexibility of the new proposed distribution.

Keywords—Odd Lindley, Rayleigh, Distribution, Entropy

The current research investigates the use of logistic regression as a statistical technique for modelling real blood pressure (BP) data. This study uses a dataset collected from a desert community in southwestern Libya. Six factors that are widely believed to play an important role in the process of BP were considered. Statistical analyses of the available dataset revealed that the main cause of hypertension in such community is age. The proposed multiple logistic regression analysis also revealed that two factors, age and systolic BP, showed greater significance among the six examined variables. These two variables were identified as having a significant effect on blood pressure performance. Based on a determined criterion, each page as the main cause of hypertension in such community participants was classified as hypertensive or not, significant variables were selected based on the p-value associated with the model significance level, and these factors were selected based on the criteria to achieve the model significance level (p < 0.05). The statistical analysis was carried out using R language.

The concepts of partial size-and-shape and partial shape are defined, with motivation from a study in humanmovementanalysis. Someco-ordinates for partial shape for landmarks in three dimensions are given, and Gaussian models for the landmark co-ordinates are proposed. The main results involve the derivation of the partial size-and-shape distributions for the isotropic and general multivariate normal models for three-dimensional data. The partial shape distribution is given in the isotropic case. Maximum likelihood based inference is explored, and examples using simulated and real human movement data illustrate the methodology.

We consider the Bayesian analysis of human movement data, where the subjects perform various reaching tasks. A set of markers is placed on each subject and a system of cameras records the three-dimensional Cartesian co-ordinates of the markers during the reaching movement. It is of interest to describe the mean and variability of the curves that are traced by the markers during one reaching movement, and to identify any differences due to covariates. We propose a methodology based on a hierarchical Bayesian model for the curves. An important part of the method is to obtain identifiable features of the movement so that different curves can be compared after temporal warping. We consider four landmarks and a set of equally spaced pseudolandmarks are located in between. We demonstrate that the algorithm works well in locating the landmarks, and shape analysis techniques are used to describe the posterior distribution of the mean curve. A feature of this type of data is that some parts of the movement data may be missing-the Bayesian methodology is easily adapted to cope with this situation.