scholarly journals A Bayesian via Laplace Approximation on Log-gamma Model with Censored Data

2016 ◽  
Vol 11 (1) ◽  
pp. 14
Author(s):  
Madaki Umar Yusuf ◽  
Mohd Rizam Abu Bakar ◽  
Qasim Nasir Husain ◽  
Noor Akma Ibrahim ◽  
Jayanthi Arasan
Keyword(s):  
Censored Data ◽  
Gamma Distribution ◽  
Survival Function ◽  
Real Life ◽  
Bayesian Regression ◽  
Survival Models ◽  
Gamma Model ◽  
Real Life Data ◽  
Mcmc Simulation ◽  
Random Walk Metropolis Algorithm

Log-gamma distribution is the extension of gamma distribution which is more flexible, versatile and provides a great fit to some skewed and censored data. Problem/Objective: In this paper we introduce a solution to closed forms of its survival function of the model which shows the suitability and flexibility towards modelling real life data. Methods/Analysis: Alternatively, Bayesian estimation by MCMC simulation using the Random-walk Metropolis algorithm was applied, using AIC and BIC comparison makes it the smallest and great choice for fitting the survival models and simulations by Markov Chain Monte Carlo Methods. Findings/Conclusion: It shows that this procedure and methods are better option in modelling Bayesian regression and survival/reliability analysis integrations in applied statistics, which based on the comparison criterion log-gamma model have the least values. However, the results of the censored data have been clarified with the simulation results.

scholarly journals Odoma Distribution and Its Application

2019 ◽  
pp. 1-11
Author(s):  
C. C. Odom ◽  
M. A. Ijomah
Keyword(s):  
Goodness Of Fit ◽  
Residual Life ◽  
Survival Function ◽  
Real Life ◽  
Rate Function ◽  
Mean Residual Life ◽  
Mathematical Properties ◽  
Real Life Data ◽  
Method Of Maximum Likelihood ◽  
New Distribution

In this study, a new continuous one parameter lifetime distribution is proposed. Its mathematical properties such as moments, order statistics, entropy, survival function, hazard rate function and mean residual life function are derived. The new distribution is applied to real-life data from engineering and the method of maximum likelihood is used to estimate the parameter. The goodness-of-fit of the new distribution shows its better fit to the data than some competing distributions.

Regularisation in discrete survival models: A comparison of lasso and gradient boosting

2021 ◽  
Vol 55 (1) ◽  
pp. 29-44
Author(s):  
Alphonce Bere ◽  
Godfrey H. Sithuba ◽  
Coster Mabvuu ◽  
Retang Mashabela ◽  
Caston Sigauke ◽  
...  
Keyword(s):  
Hazard Function ◽  
False Positive Rate ◽  
Real Life ◽  
Survival Models ◽  
Gradient Boosting ◽  
Penalization Method ◽  
Baseline Hazard ◽  
Real Life Data ◽  
Positive Rate ◽  
The Mean

We present the results of a simulation study performed to compare the accuracy of a lasso-type penalization method and gradient boosting in estimating the baseline hazard function and covariate parameters in discrete survival models. The mean square error results reveal that the lasso-type algorithm performs better in recovering the baseline hazard and covariate parameters. In particular, gradient boosting underestimates the sizes of the parameters and also has a high false positive rate. Similar results are obtained in an application to real-life data.

scholarly journals On the Properties and Applications of Lomax-Exponential Distribution

2018 ◽  
pp. 1-13
Author(s):  
Terna Godfrey Ieren ◽  
David Adugh Kuhe
Keyword(s):  
Exponential Distribution ◽  
Survival Function ◽  
Real Life ◽  
Likelihood Estimation ◽  
Random Variable ◽  
Quantile Function ◽  
Life Data ◽  
Life Problems ◽  
Real Life Data ◽  
New Distribution

The Exponential distribution is memoryless and has a constant failure rate which makes it unsuitable for real life problems. This paper introduces a new distribution powered by an exponential random variable which gives a more flexible model for modelling real-life data. This new extension of the Exponential Distribution is called “Lomax-Exponential distribution (LED)”. The extension of the new distribution became possible with the help of a Lomax generator proposed by [1]. This paper derives and studies some expressions for various statistical properties of the new distribution including distribution function, moments, quantile function, survival function and hazard function known as reliability functions. The inference for the Lomax-Exponentially distributed random variable were investigated based on some plots of the distribution and others revealed its behaviour and usefulness in real life situations. The parameters of the distribution are estimated using the method of maximum likelihood estimation. The performance of the new Lomax-Exponential distribution has been tested and compared to the Weibull-Exponential, Transmuted Exponential and the conventional Exponential distribution using three real life data sets.  

scholarly journals Samade Probability Distribution: Its Properties and Application to Real Lifetime Data

2021 ◽  
pp. 1-11
Author(s):  
Samuel Aderoju
Keyword(s):  
Survival Function ◽  
Real Life ◽  
Likelihood Estimation ◽  
Reliability Function ◽  
Entropy Measure ◽  
Lifetime Distributions ◽  
Mathematical Properties ◽  
Real Life Data ◽  
Proposed Model ◽  
First Four Moments

A new two-parameter lifetime distribution has been proposed in this study. The distribution is called Samade distribution. The model is motivated by the wide use of the lifetime models derived from the mixture of gamma and exponential distributions. Its mathematical properties which include the first four moments, variance as well as coefficient of variation, reliability function, hazard function, survival function, Renyi entropy measure and distribution of order statistics have been successfully derived. The maximum likelihood estimation of its parameters and application to real life data have been discussed. Application of this model to three real datasets shown that the proposed model yields a satisfactorily better fit than other existing lifetime distributions. The comparism of goodness-of-fits were established using -2Loglikelihood, AIC and BIC. 

Neuropsychology in the Real World

2014 ◽  
Vol 25 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Martin Peper ◽  
Simone N. Loeffler
Keyword(s):  
Neuropsychological Assessment ◽  
Real World ◽  
Ecological Validity ◽  
Real Life ◽  
Emotional States ◽  
Context Sensitive ◽  
Traditional Assessment ◽  
Life Data ◽  
Real Life Data ◽  
Assessment And Treatment

Current ambulatory technologies are highly relevant for neuropsychological assessment and treatment as they provide a gateway to real life data. Ambulatory assessment of cognitive complaints, skills and emotional states in natural contexts provides information that has a greater ecological validity than traditional assessment approaches. This issue presents an overview of current technological and methodological innovations, opportunities, problems and limitations of these methods designed for the context-sensitive measurement of cognitive, emotional and behavioral function. The usefulness of selected ambulatory approaches is demonstrated and their relevance for an ecologically valid neuropsychology is highlighted.

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