An Alternative Approach to AIC and Mallow’s Cp Statistic-Based Relative Influence Measures (RIMS) in Regression Variable Selection

Uzoma, Umeh Edith; Jeremiah, Obulezi Okechukwu

doi:10.4236/ojs.2016.61009

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…of the EPLLD using data on the survival times of guinea pigs injected with different amount of tubercle bacilli. This is because their standard errors are minimum [23,24,25,26].…”

Section: Numerical Applicationmentioning

confidence: 99%

Comparative Study Based on Simulation of Some Methods of Classical Estimation of the Parameters of Exponentiated Lindley – Logarithmic Distribution

Musa

Onyeagu

Obulezi

2023

AJPAS

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In this article, some non-bayesian estimation procedures of the Exponentiated Power Lindley-Logarithmic Distribution for modeling real life data are considered. A simulation study is carried out to determine the best among others using the root meas squared error values. Real life data are used to substantiate the result from the simulation study. It was discovered from simulation study and real data that the weighted least squares estimation procedure is the best method for estimating the parameters of the Exponentiated Power Lindley-Logarithmic distribution.

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“…of the EPLLD using data on the survival times of guinea pigs injected with different amount of tubercle bacilli. This is because their standard errors are minimum [23,24,25,26].…”

Section: Numerical Applicationmentioning

confidence: 99%

Comparative Study Based on Simulation of Some Methods of Classical Estimation of the Parameters of Exponentiated Lindley – Logarithmic Distribution

Musa

Onyeagu

Obulezi

2023

AJPAS

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“…It has been used in many fields including econometrics, chemistry, and engineering (Gruber, & Schucany, 2020). Uzoma, & Jeremiah, (2016) developed outlier detection and optimal variable selection techniques in regression analysis and other fascinating papers by the authors include (Anabike et al, 2023;Innocent et al, 2023;Abuh, Onyeagu, & Obulezi, 2023a;Abuh, Onyeagu, & Obulezi, 2023b;.…”

Section: Suggested Citationmentioning

confidence: 99%

Prediction of House Prices in Lagos-Nigeria Using Machine Learning Models

Nwankwo,

Onyeizu,

Asogwa

et al. 2023

ejtas

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This paper considers the relationship between the price of houses and the features namely the number of bedrooms, parking space, and different house types. In this study, a machine learning approach was used to develop prediction models that predicted house prices in Lagos. Different machine learning techniques were used, train-test split to split the data into training sets for training and building the model and test data to test the accuracy of the model, performance metric mean absolute error to set the baseline for the model, Variance Inflation Factor (VIF) to help remove multicollinearity between features and Streamlit interactive dashboards to communicate with the model. Correlation and regression methods were used to examine the relationship and build the model. It is observed that there is a strong positive correlation between the number of bedrooms and the number of toilets, likewise the number of bedrooms and the number of bathrooms. It also shows that there is a moderate positive correlation between the number of bedrooms and price. The model shows that the number of bedrooms, parking spaces, and house types play an important role in determining the price of houses.

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“…Hannan-Quinn Information Criterion, and Kolmogorov-Smirnov (K-S) statistics, is best among others. See Uzoma and Obulezi (Uzoma& Obulezi, 2016) for relevant modification on model performance criteria using Bayesian Information Criterion (BIC). From table 2, the SR distribution has a better fit to the data on blood cancer (Leukemia) from one the health hospital in Saudi Arabia, since its probability value is the largest among other probabilities that are greater than 0.05.…”

Section: Data Set On Blood Cancer Leukemiamentioning

confidence: 99%

A New Variant of Rama Distribution with Simulation Study and Application to Blood Cancer Data

Omoruyi,

Omeje,

Anabike

et al. 2023

ejtas

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In this paper, we propose a new lifetime distribution with flexibility in modeling than its parent distribution. The new distribution is a variant of the Rama distribution having a positive shift parameter. We call the proposed distribution Shifted Rama (SR) distribution. Mathematical and statistical characteristics such as crude moments, central moment, coefficient of variation, index of dispersion, conditional moment, mean residual life function, mean deviation, Bonferroni and Lorenz curve, and the order statistics are derived. Furthermore, reliability measures like survival function, hazard function have been derived. Estimation techniques namely; the maximum likelihood, least squares, weighted least squares, maximum product spacing, Cramer-von- Mises, Anderson-Darling and the right-tailed Anderson-Darling estimations are used. To demonstrate the applicability of the distribution, a numerical example was the blood cancer data from Ministry Hospital in Saudi Arabia. Based on the results, the proposed distribution performed better than the competing distributions. Simulation of the Estimates of the parameters based on the classical methods considered are obtained, and result showed that the maximum likelihood estimator gave the best classical estimates of the parameters compared to other methods considered.

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An Alternative Approach to AIC and Mallow’s Cp Statistic-Based Relative Influence Measures (RIMS) in Regression Variable Selection

Cited by 7 publications

References 17 publications

Comparative Study Based on Simulation of Some Methods of Classical Estimation of the Parameters of Exponentiated Lindley – Logarithmic Distribution

Comparative Study Based on Simulation of Some Methods of Classical Estimation of the Parameters of Exponentiated Lindley – Logarithmic Distribution

Prediction of House Prices in Lagos-Nigeria Using Machine Learning Models

A New Variant of Rama Distribution with Simulation Study and Application to Blood Cancer Data

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