Multivariate normal mean-variance mixture distribution based on Lindley distribution

Naderi, Mehrdad; Arabpour, Alireza; Jamalizadeh, Ahad

doi:10.1080/03610918.2017.1307400

Cited by 13 publications

(8 citation statements)

References 20 publications

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“…The R code of the real data analysis can be found from the authors upon request. An interesting extension of the current work that deserves attention in future research concerns the multivariate case of NMVL-MIX (Naderi et al, 2017). Also, The use of mixtures of factor analyzers can be considered as a parsimonious modeling approach.…”

Section: Resultsmentioning

confidence: 99%

Mixture of Normal Mean-Variance of Lindley Distributions

Naderi¹,

Arabpour²,

Jamalizadeh³

2017

JSRI

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Abstract. In this paper, a new mixture modelling using the normal meanvariance mixture of Lindley (NMVL) distribution has been considered. The proposed model is heavy-tailed and multimodal and can be used in dealing with asymmetric data in various theoretic and applied problems. We present a feasible computationally analytical EM algorithm for computing the maximum likelihood estimates. The behavior of the obtained maximum likelihood estimators is studied with respect to bias and mean squared errors through conducting a simulation study. Two examples with flow cytometry data are used to illustrate the applicability of the proposed model.

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Section: Resultsmentioning

confidence: 99%

Mixture of Normal Mean-Variance of Lindley Distributions

Naderi¹,

Arabpour²,

Jamalizadeh³

2017

JSRI

Self Cite

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“…Because a categorical training is aimed; mvmn distribution was used in training. [20] Then the result struct was created with predict function. After that the code for five-fold cross-validation was made.…”

Section: Methodsmentioning

confidence: 99%

Determining the Risky Software Projects using Artificial Neural Networks

Sakucoglu¹,

Valizada²,

Olcaysoy³

et al. 2022

IJSEA

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Determining risky software projects early is a very important factor for project success. In this study it is aimed to choose the most correctly resulting modelling method that will be useful for early prediction of risky software projects to help companies to avoid losing time and money on unsuccessful projects and also facing legal requirements because of not being able to fullfill their responsibilites to their customers While making the research for this subject, it is seen that in previous researches, usually traditional modelling techniques were preferred. But it is observed that these methods were mostly resulted with high misclassification ratio. To overcome this problem, this study proposes a three-layered neural network (NN) architecture with a backpropagation algorithm. NN architecture was trained by using two different data sets which were OMRON data set (collected by OMRON) and 2016-2020 ES.LV data set (collected by the authors) separately. For the made of this study firstly the most relevant classification method (Gaussian Naive Bayes Algorithm) and the most relevant neural network method (Scaled Conjugate Gradient Backpropagation Algorithm) was chosen and both data sets were trained by using each method seperately for the purpose of observing which type of modelling architecture would give better results. Experimental results of this study showed that the neural network approach is useful for predicting whether a project is risky or not risky.

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“…In particular, the fitting of the NMVBS and NIG distributions is generally more efficient than the GHST distribution because the whole parameters of the NMVBS and NIG distributions can be estimated in analytical expressions. By contrast, the VG and NMVL are known to be more suited to the data with lightly skewed but highly peaked features (Hu, 2005; Naderi et al, 2017). Parameter estimation for the NMVL distribution can be implemented faster than the VG distribution because of existence of closed‐form expressions for the whole NMVL parameters in the iterative estimating procedure.…”

Section: Notation and Preliminariesmentioning

confidence: 99%

Multivariate measurement error models with normal mean‐variance mixture distributions

Mirfarah

Naderi

Lin

et al. 2022

Stat

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The class of normal mean‐variance mixture (NMVM) distributions is a rich family of asymmetric and heavy‐tailed distributions and has been widely considered in parametric modeling of the data for robust statistical inference. This paper proposes an extension of measurement error models by assuming the NMVM distributions for the unobserved covariates and error terms in the model, referred to as the NMVM‐MEM. An expectation conditional maximization either (ECME) algorithm is developed to compute the maximum likelihood (ML) estimates of model parameters. Furthermore, an information‐based approach is performed to derive the asymptotic covariance matrix of ML estimators. The analysis of a blood pressure dataset illustrates the superiority of NMVM‐MEM to accommodate asymmetry and outliers over the normal counterpart. Two simulation studies are undertaken to validate our proposed techniques.

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Multivariate normal mean-variance mixture distribution based on Lindley distribution

Cited by 13 publications

References 20 publications

Mixture of Normal Mean-Variance of Lindley Distributions

Mixture of Normal Mean-Variance of Lindley Distributions

Determining the Risky Software Projects using Artificial Neural Networks

Multivariate measurement error models with normal mean‐variance mixture distributions

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