A new count model generated from mixed Poisson transmuted exponential family with an application to health care data

Bhati, Deepesh; Kumawat, Pooja; Gómez–Déniz, Emilio

doi:10.1080/03610926.2016.1257712

Cited by 27 publications

(11 citation statements)

References 51 publications

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“…Examples exist of methods that use robust versions of the negative binomial framework ( 12–14 ). More broadly, a large number of robust (long-tailed) gamma-compound distributions exist ( 15–17 ); however, the implementation of statistical models from many of those is not trivial and often require non-efficient computations as a closed-form of the probability density does not always exist. Considering that by far the most used methods for differential gene transcriptional abundance are edgeR ( 2 ) and DESeq2 ( 3 ) (23rd and 26th top downloaded packages in R/Bioconductor repository; bioconductor.org/packages/stats accessed June 2020), to develop an independent evaluation tool for identifying transcripts that may have unreliable statistics is extremely relevant.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic outlier identification for RNA sequencing generalized linear models

Mangiola

Thomas

Modrák

et al. 2021

NAR Genomics and Bioinformatics

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Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers.

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Section: Introductionmentioning

confidence: 99%

Probabilistic outlier identification for RNA sequencing generalized linear models

Mangiola

Thomas

Modrák

et al. 2021

NAR Genomics and Bioinformatics

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“…To achieve this goal, PTE distribution, proposed by Bhati et al (2017), is used as an innovation process of the INAR(1) process since the concerned distribution has been found suitable to handle over-dispersion in various application areas. The statistical properties of the proposed INAR(1) process are studied comprehensively.…”

Section: Introductionmentioning

confidence: 99%

Modelling with the Novel INAR(1)-PTE Process

Altun

Khan

2021

Methodol Comput Appl Probab

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In this paper, the first-order non-negative integer-valued autoregressive process with Poisson-transmuted exponential innovations is introduced. Three estimation methods, namely, the conditional maximum likelihood, conditional least squares and Yule-Walker estimation methods are discussed to estimate the unknown parameters of the proposed process. Additionally, the simulation study is presented to assess the efficiencies of these estimation methods. Applications to two real-life data sets illustrate the usefulness of the proposed process.

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“…generalized geometric by Gómez-Déniz (2010), discrete generalized exponential type II by Nekoukhou et al (2013), discrete Rayleigh (DR) by Roy (2004) Atikankul et al (2020) and Poissontransmuted exponential distribution by Bhati et al (2017).…”

Section: Introductionmentioning

confidence: 99%

A New Three-Parameter Discrete Distribution With Associated INAR(1) Process and Applications

et al. 2020

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The aim of this article is to propose a new three-parameter discrete Lindley distribution. A wide range of its structural properties are investigated. This includes the shape of the probability mass function, hazard rate function, moments, skewness, kurtosis, index of dispersion, mean residual life, mean past life and stress-strength reliability. These properties are expressed in explicit forms. The maximum likelihood approach is used to estimate the model parameters. A detailed simulation study is carried out to examine the bias and mean square error of the estimators. Using the proposed distribution, a new first-order integervalued autoregressive process is introduced for the over-dispersed, equi-dispersed and under-dispersed time series of counts. To demonstrate the importance of the proposed distribution, three data sets on coronavirus, length of stay at psychiatric ward and monthly counts of larceny calls are analyzed. INDEX TERMS Survival discretization method, over-dispersion, INAR(1) process, simulation.

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A new count model generated from mixed Poisson transmuted exponential family with an application to health care data

Cited by 27 publications

References 51 publications

Probabilistic outlier identification for RNA sequencing generalized linear models

Probabilistic outlier identification for RNA sequencing generalized linear models

Modelling with the Novel INAR(1)-PTE Process

A New Three-Parameter Discrete Distribution With Associated INAR(1) Process and Applications

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