Bayesian Hypothesis Testing of Two Normal Samples using Bootstrap Prior Technique

Olaniran, Oyebayo Ridwan; Yahya, Waheed Babatunde

doi:10.22237/jmasm/1509496440

Cited by 20 publications

(15 citation statements)

References 22 publications

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“…They suggested objective prior through Bayes factor as an alternative approach to handle one-way ANOVA model. Objective Bayes (let the data speak for themselves) are no way better than the classical approach as its often used when subjective priors are difficult to compute or elicit (Yahya, Olaniran, and Ige 2014;Olaniran, Olaniran, Yahya, Banjoko, Garba, Amusa, and Gatta 2016;Olaniran and Yahya 2017;Olaniran, Abdullah, Pillay, and Olaniran 2018). Thus, in this paper, we developed a Bayesian one-way ANOVA test function using bootstrap prior Olaniran and Yahya (2017) for variable selection in multiclass classification problem.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Variable Selection for Multiclass Classification using Bootstrap Prior Technique

Olaniran

Abdullah

2019

AJS

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In this paper, the one-way ANOVA model and its application in Bayesian multi-class variable selection is considered. A full Bayesian bootstrap prior ANOVA test function is developed within the framework of parametric empirical Bayes. The test function developed was later used for variable screening in multiclass classification scenario. Performance comparison between the proposed method and existing classical ANOVA method was achieved using simulated and real life gene expression datasets. Analysis results revealed lower false positive rate and higher sensitivity for the proposed method.

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Section: Introductionmentioning

confidence: 99%

Bayesian Variable Selection for Multiclass Classification using Bootstrap Prior Technique

Olaniran

Abdullah

2019

AJS

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“…This is opposite to the property of shortrange dependence (SRD), where the autocorrelation function decays exponentially, and the equation 2has a finite value. Short and long-range dependence have a common relationship with the value of the Hurst parameter (H) of the self-similar process [14,26,16,17,18,11,20,19,21]. When the value of H lies in the interval 0 < H < 0.5, the self-similar process is said to be SRD and if the value of H lies in the interval 0.5 < H < 1 the process is said to have LRD.…”

Section: Long-range Dependence (Lrd)mentioning

confidence: 99%

Generalized Self-Similar First Order Autoregressive Generator (GSFO-ARG) for Internet Traffic

Popoola

Yahya

Popoola

et al. 2020

Stat., optim. inf. comput.

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Internet traffic data such as the number of transmitted packets and time spent on the transmission of Internet protocols (IPs) have been shown to exhibit self-similar property which can contain the long memory property, particularly in a heavy Internet traffic. Simulating this type of dataset is an important aspect of delay avoidance planning, especially when trying to mimic real-life processing of packets on the Internet. Most of the existing procedures often assumed the process follows a Gaussian distribution, and thus long memory processes such as Fractional Brownian Motion (FBM) and Fractional Gaussian Noise (FGN) among others are used. These approaches often result in estimation errors arising from the use of inappropriate distribution. However, it has been established that the distribution of Internet processes are heavy-tailed. Therefore, in this paper, a new method that is capable of generating heavy-tailed self-similar traffic is proposed based on the first-order autoregressive AR (1) process. The proposed method is compared with some of the existing methods at varying values of the self-similar index and sample sizes. The imposed self-similarity indices were estimated using the Range/Standard deviation statistic (R/S). Performance analysis was achieved using the absolute percentage errors. The results showed that the proposed method has a lower average error when compared with other competing methods.

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“…The topics in the classes include sports, politics, religion etc., which is diverse enough. The Precision (P) was used as class-specific index while Recall (R) (also known as sensitivity) is the proportion of the total amount of relevant cases that were actually retrieved [23][24][25][26][27][28][29]. The F 1 is a measure of the accuracy of the test dataset and it is defined as:…”

Section: Performance Evaluation Using 20-newgroup Datasetmentioning

confidence: 99%

GPLDA: A Generalized Poisson Latent Dirichlet Topic Model

Bala¹,

Saringat²

2019

IJACSA

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The earliest modification of Latent Dirichlet Allocation (LDA) in terms of words or document attributes is by relaxing its exchangeability assumption via the Bag-of-word (BoW) matrix. Several authors have proposed many modifications of the original LDA by focusing on model that assumes the current topic depends on the words from previous topic. Most of the earlier work ignored the document length distribution since it is assumed that it will fizzle out at the modelling stage. Thus, in this paper, the Poisson document length distribution of LDA model is replaced with Generalized Poisson (GP) distribution which has the strength of capturing complex structures. The main strengths of GP are in capturing overdispersed (variance larger than mean) and under dispersed (variance smaller than mean) count data. The Poisson distribution used by LDA strongly relies on the assumption that the mean and variance of document lengths are equal. This assumption is often unrealistic with most real-life text data where the variance of document length may be greater than or less than their mean. Approximate estimate of the GPLDA model parameters was achieved using Newton-Raphson approximation technique of log-likelihood. Performance and comparative analysis of GPLDA with LDA using accuracy and F 1 showed improved results.

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Bayesian Hypothesis Testing of Two Normal Samples using Bootstrap Prior Technique

Cited by 20 publications

References 22 publications

Bayesian Variable Selection for Multiclass Classification using Bootstrap Prior Technique

Bayesian Variable Selection for Multiclass Classification using Bootstrap Prior Technique

Generalized Self-Similar First Order Autoregressive Generator (GSFO-ARG) for Internet Traffic

GPLDA: A Generalized Poisson Latent Dirichlet Topic Model

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