Objectives: This study tries to find the associations of Creatinine ratio (Cr) with HbA1c and lipid profile parameters based on a real dataset.

Materials & Methods: A real data set has been considered with 1000 individuals, this data set can be found at https://data.mendeley.com/datasets/wj9rwkp9c2/1. Using statistical joint generalized linear models, the creatinine ratio probabilistic model has been derived.

Results: From the fitted Log-normal model, the mean Cr is positively associated with Urea (P=0.0004) and is indifferent to HbA1c (P=0.9299), but it is positively associated with the joint interaction effect (JIE) of HbA1c and Urea, i.e., of HbA1c*Urea (P=0.0197). Mean Cr is indifferent to Chol (P=0.8158), while it is positively associated with the JIE of HbA1c*Chol (P=0.0001). Mean Cr is negatively associated with HDL (P=0.0417), TG (P=0.0045), the JIE of HbA1c*HDL (P<0.0001), and the JIE of HbA1c*TG (P=0.0030). Mean Cr is positively associated with the JIE HDL*Chol (P=0.0003). Mean Cr is indifferent to LDL (P=0.3080), while it is negatively associated with the JIE of Chol and LDL, i.e., of Chol*LDL (P=0.0240). Variance of Cr is positively associated with both the marginal effects BMI (P=0.0185) and Chol (P=0.0092), but it is negatively associated with the JIE of BMI*Chol (P=0.0079). Variance of Cr is positively associated with TG (P=0.0061) and Urea (P<0.0001).

Conclusions: On the basis of the above real data set, it is established that creatinine ratio maintains a complex relationship with HbA1c and lipid profile parameters along with their joint interaction effects.

KEYWORDS: Creatinine ratio (Cr), Hemoglobin A1c (HbA1c), Lipid profile parameters, joint generalized linear models (JGLMs).

Stress-strength models have special importance in reliability literature and engineering applications. This paper consists of the estimation problem of a stress-strength model with a multi-component system, i.e., a system that can be regarded to be alive if at least s out of k (s ≤ k) strength components exceed the stress component. The reliability of such a system has been obtained when both the stress and the strength variables have Geometric distributions. UMVUE of Rs,k is obtained based on upper record values. Bayesian estimators under the squared error loss function using the conjugate beta prior distributions have been obtained. A simulation study has been implemented to assess the performance of estimates.

KEYWORDS: Stress-strength, Multi-component, Upper record values, UMVUE, Loss function.

Onion, a staple vegetable, plays a crucial role in both the economy and the livelihoods of people; especially the farmers. Accurate price forecasting is vital for market stakeholders, including consumers, farmers, and policymakers. However, onion prices exhibit fluctuations and multiple seasonal patterns, making prediction challenging. This study delves into a comprehensive analysis of onion prices in the Pune region of Maharashtra using the Dynamic Harmonic Regression (DHR) model, a sophisticated time series technique, adept at capturing multiple seasonalities. The DHR model outperforms traditional forecasting methods, such as Holt-Winters and ARIMA, in predictive accuracy. Furthermore, cointegration analysis examines the
relationship between onion prices in the Pune and Vashi markets, providing a deeper understanding of market dynamics. By leveraging historical price data, the study offers valuable insights into future price trends, enabling farmers to make informed
decisions and develop strategies to mitigate price fluctuations. To further support farmers, an algorithm is developed to identify the optimal day to sell the produce from the farmer’s perspective. Empowering farmers with this forecasting tool can significantly enhance decision-making with actionable price forecasts.

KEYWORDS: Agriculture, Cointegration, Decision Making, Farmer, Time Series Analysis.

We generalize fractional Ornstein-Uhlenbeck process whose driving term is another fractional Ornstein-Uhlenbeck process. The motivation is related to stochastic volatility model. We estimate the parameters of both processes by maximum likelihood method and minimum contrast method. We obtain strong consistency and asymptotic normality of the estimators as the time length of observation becomes large.

KEYWORDS: Stochastic differential equation, fractional Brownian motion, fractional Ornstein-Uhlenbeck process, correlation, volatility, maximum likelihood estimator, minimum contrast estimator, Durbin-Watson statistic.