On the Characteristics and Application of Inverse Power Pranav Distribution

Nwankwo, Kingsley C.; Onyeagu, I. S.; Nwankwo, Chike Henry; Osuji, George A.; Onyekwere, Chrisogonus K.

doi:10.9734/jamcs/2021/v36i730384

Cited by 5 publications

(6 citation statements)

References 2 publications

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“…From Table 6, the weighted Least Squares estimator (WLSE) gives the best estimates since its standard errors are the least among others. The next application is on the survival times of guinea pigs injected with different amount of tubercle bacilli studied by Bjerkedal [22], Anabike et al [1] and Onyekwere and Obulezi [5]. of the EPLLD using data on the survival times of guinea pigs injected with different amount of tubercle bacilli.…”

Section: Numerical Applicationmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Numerical Applicationmentioning

confidence: 99%

“…A common indicator of a good estimate is how small its standard error is. Anabike et al [1] used some classical methods and bayesian method to estimate the parameters of Zubair-Exponential distribution. Dey et al [2] explored different estimation procedures for estimating Dagum distribution.…”

Section: Introductionmentioning

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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“…They provided the properties of the distribution and applied it to real data. To acquire more information on inverse distributions, researchers may read the following articles by [7][8][9]. Having looked at some existing distributions and their properties, the authors decide to suggest a more robust distribution that can compete with any other distribution, based on Shanker distribution.…”

Section: Original Research Articlementioning

confidence: 99%

On Modified Inverse Shanker Distribution and Applications

Nwadiogbu

Eze

Onyekwere

2022

AJPAS

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Due to variations in life occurrences, descriptions or interpretations and predictions with some level of accuracy has become challenging. In order to use models to solve these problems, statisticians have provided numerous number of probability distributions which can be used to describe one situation or the other. Rama shanker provided Shanker distribution which is not flexible enough to accommodate datasets with decreasing function. In order to add flexibility to Shankers’ distribution, the aim of this article is to suggest a new model developed by modifying shankers’ distribution. The new distribution will be called “Modified inverse Shanker distribution”. It has one special case, inverse Shanker distribution. Besides the basic properties of the distribution, the maximum likelihood technique of estimating the parameters of the distribution and some of the reliability measures are also discussed. We also illustrate the applicability of the proposed distribution using two real datasets.

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On the Characteristics and Application of Inverse Power Pranav Distribution

Cited by 5 publications

References 2 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

On Modified Inverse Shanker Distribution and Applications

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