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Journal of Mathematics and Statistics Studies
Number of Followers: 0

This is an Open Access Journal

Open Access journal
ISSN (Online) 2709-4200
Published by Al-Kindi Center for Research and Development

[14 journals]

The Efficiency of the Proposed Smoothing Method over the Classical Cubic
Smoothing Spline Regression Model with Autocorrelated Residual
- Authors: Samuel Olorunfemi Adams; Omorogbe J. Asemota
  Abstract: Spline smoothing is a technique used to filter out noise in time series observations when predicting nonparametric regression models. Its performance depends on the choice of the smoothing parameter. Most of the existing smoothing methods applied to time series data tend to overfit in the presence of autocorrelated errors. This study aims to determine the optimum performance value, goodness of fit and model overfitting properties of the proposed Smoothing Method (PSM), Generalized Maximum Likelihood (GML), Generalized Cross-Validation (GCV), and Unbiased Risk (UBR) smoothing parameter selection methods. A Monte Carlo experiment of 1,000 trials was carried out at three different sample sizes (20, 60, and 100) and three levels of autocorrelation (0.2, 05, and 0.8). The four smoothing methods' performances were estimated and compared using the Predictive Mean Squared Error (PMSE) criterion. The findings of the study revealed that: for a time series observation with autocorrelated errors, provides the best-fit smoothing method for the model, the PSM does not over-fit data at all the autocorrelation levels considered ( the optimum value of the PSM was at the weighted value of 0.04 when there is autocorrelation in the error term, PSM performed better than the GCV, GML, and UBR smoothing methods were considered at all-time series sizes (T = 20, 60 and 100). For the real-life data employed in the study, PSM proved to be the most efficient among the GCV, GML, PSM, and UBR smoothing methods compared. The study concluded that the PSM method provides the best fit as a smoothing method, works well at autocorrelation levels (ρ=0.2, 0.5, and 0.8), and does not over fit time-series observations. The study recommended that the proposed smoothing is appropriate for time series observations with autocorrelation in the error term and econometrics real-life data. This study can be applied to; non – parametric regression, non – parametric forecasting, spatial, survival, and econometrics observations.
  PubDate: Thu, 23 Feb 2023 00:00:00 +000
Food Satisfaction among Students: A Study of Present Public University
Students in Bangladesh
- Authors: Md Nurul Raihen; Sultana Akter, Md Nazmul Sardar
  Abstract: This study's primary goal is to examine the characteristics of public university canteen food service. A saying goes, "Health is riches." Therefore, it not only helps them to clear their minds but also enables them to focus on their studies, families, and careers. A model was created from the information that was provided and tested using information from a survey that was carried out at a college in northwest Pennsylvania. The findings imply that staff behavior, food quality, and price are the three key factors that affect student satisfaction. Cleanliness, responsiveness, and environment are further important factors. Considering these factors (food quality, food variety, price justice, ambiance, etc.) could help people in charge of food services provide more value and satisfaction to improve students' entire educational experience.
  PubDate: Thu, 16 Feb 2023 00:00:00 +000
The Use of Homotopy Regularization Method for Linere and Nonlinner
Fredholm Integral Equations of the First Kind
- Authors: Merajuddin Rasekh; Nooruddin Fakhri
  Abstract: Recently, Wazwaz has studied the regularization method to the one-dimensional linear Fredholm integral equations of the first kind [Wazwaz, 2011]. In this work, we develop this method for the linear and nonlinear two-dimensional Fred-holm integral equations of the first kind. Indeed, the regularization method is used for linear integral equations directly. But nonlinear integral equations of the first kind are transformed to linearintegral equations of the first kind by a change of variable; then, The Regularization-Homotopy Method is applied. The combination of the regularization method and the homotopy perturbation method, or shortly, the regularization-homotopy method, is used to find a solution to the equation. Some examples will be used to highlight the reliability of the generalized of Regularization-Homotopy Method.
  PubDate: Thu, 16 Feb 2023 00:00:00 +000

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