Bayesian Analysis of Extended Cox Model with Time-Varying Covariates Using Bootstrap Prior

Abstract

A new Bayesian estimation procedure for extended cox model with time varying covariate was presented. The prior was determined using bootstrapping technique within the framework of parametric empirical Bayes. The efficiency of the proposed method was observed using Monte Carlo simulation of extended Cox model with time varying covariates under varying scenarios. Validity of the proposed method was also ascertained using real life data set of Stanford heart transplant. Comparison of the proposed method with its competitor established appreciable supremacy of the method.

A Generalized Residual-Based Test for Fractional Cointegration in Panel Data with Fixed Effects

“…We consider cases with ρ = 0.0, 0.5 and sample sizes T = 500, 1250, 2500 corresponding to t = 100, 250, 1000. Similar approaches were used in [4,12,[22][23][24][25][26][27].…”

A Comparative Analysis of Semiparametric Tests for Fractional Cointegration in Panel Data Models

“…The empircal type I error rates and power for the six tests were calculated using the average of the 5000 runs of each simulated model and testing. Similar procedure was used in Jamil, Abdullah, Kek, Olaniran, and Amran (2017); Olaniran and Yahya (2017); Abdullah (2019b, 2020); Popoola, Yahya, Popoola, and Olaniran (2020); Olaniran and Abdullah (2019a). For brevity, each test is presented in tables and figures as authors' initial letters and the corresponding publication year.…”

Bayesian Additive Regression Trees for Predicting Colon Cancer: Methodological Study (Validity Study)

“…Several algorithmic procedures have been applied to solve various experimental problems that involve simulation and modelling of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) proteins. [1][2][3][4][5][6] The DNA and RNA are essential biological measurements used to monitor the abnormal cell growth in genetic sequencing, which serves as the bedrocks in non-clinical diagnoses.…”

“…This form of analysis is suboptimal because it ignores the interaction between genes. 5,6 The decision trees algorithm proffers an interim solution to this issue. However, its performance is not stable based on findings from several empirical studies.…”

“…14 Moreover, Bayesian approaches are the new emerging probabilistic approach that is realistic and provides lower error than other classical maximum likelihood-based approaches. [4][5][6][15][16][17][18][19] Therefore, in this paper, we aim to compare the predictive performance of the Bayesian additive regression trees Bayesian Additive Regression Trees (BART) with RF and gradient boosting machine (GBM) using micro-array colon cancer data. 14…”