SCOPUA Journal of Applied Statistical Research
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Joint Parameter Estimation in Measurement Errors and Non-Response for Sensitive Variables
Get PDFIn this paper, joint parameter estimation for mean and variance was considered in the presence of measurement errors and non-response for the Sensitive variable using auxiliary information. The properties of the suggested estimator(s) have been studied. Expressions for bias and mean square error up to first order of approximation are derived, and the theoretical characteristics of the suggested estimators are scrutinised. A numerical study is carried out to observe the performance of the proposed estimators. The scope of this work is to create better estimators that can effectively manage both kinds of mistakes at the same time, especially in randomised response settings where biasing sensitive data is common. Applications to actual data and simulation studies are used to evaluate the estimators\u27 effectiveness. When both measurement error and non-response are present, the results show that the suggested estimators outperform the traditional estimators
The Almon Liu-Type M-Estimator for the Distributed Lag Models in the Presence of Multicollinearity and Outliers
Get PDFThe Almon method is widely used for the estimation of the distributed lag models (DLM). The advantage of using the Almon technique lies in its capability to avoid some serious problems that may arise from the direct application of ordinary least squares (OLS). In the Almon technique, the OLS procedure is applied on transformed regressors, and these regressors correlate themselves leading to the problem of multicollinearity. Moreover, in the presence of outliers in the y-direction, the Almon estimator (AE) may become sensitive. The presence of multicollinearity and outliers jointly in the dataset can strongly distort the AE, leading to the unreliable estimation of the lagged coefficients. We propose the Almon Liu-type M-estimator (ALTME) to address the joint issue of multicollinearity and outliers in y-direction. To show that the proposed estimator has an advantage over the AE, the Almon M-estimator (AME), and the Almon ridge M-estimator (ARME), the Monte Carlo Simulation and two real-life numerical examples are given
Modelling Wind Speed at Ikeja Station using Skewed Statistical Distributions
Get PDFWind speed, a key atmospheric parameter, results from the movement of air from high- to low-pressure regions driven primarily by temperature variations. This study modelled the wind speed (m/s) of Ikeja, Lagos State, using data from 2000 to 2020 through statistical techniques such as descriptive statistics, data visualization, and goodness-of-fit (GOF) tests, including chi-square, Kolmogorov-Smirnov, Anderson-Darling, and Cramer-von Mises analysis. Three positively skewed distributions, Gamma, Weibull, and Log-normal, were evaluated. Descriptive analysis indicated that the dataset was predominantly right-skewed (Skp > 0). The GOF results show that the Weibull distribution provides the best representation of the wind speed data (p=0.02), followed by the distributions of Log-normal and Gamma. The Weibull parameter (α > 1) further confirmed its suitability for the data. The findings suggest that Ikeja may experience higher wind speeds in the future, emphasizing the need for precautionary measures to mitigate potential damage to infrastructure and property. This study provides valuable insights for meteorologists and urban planners in anticipating and managing climate-related risks in the city