Hasil untuk "math.NA"

Rohmatul Fajriyah, Noodchanath Kongchouy, Wanvisa Saisanan Na Ayudhaya et al.

Bioinformatics is blooming and its data are store in some repository offline and or online. Yet some basic concepts are not fully disseminated. The paper intends to provide the reader with a review of one important concept in the pipeline bioinformatics data analysis of microarray, pre-processing. In pre-processing, there are four steps, background correction, normalization, probe correction and summarization. Each step consists of several methods, and we describe each method to give a better understanding on how it works theoretically. We focused on microarray data from Affymetrix platform with single-color chip.

Na Lei, Wei Chen, Zhongxuan Luo et al.

Mal-Soon Lee, D. G. Kanhere

I. V. Mitchell, W. N. Lennard, D. Phillips

V. V. Zhuk

Na Tang

Monthira Duangsaphon, Sukit Sokampang, Kannat Na Bangchang

<abstract><p>The discrete Weibull model can be adapted to capture different levels of dispersion in the count data. This paper takes into account the direct relationship between explanatory variables and the median of discrete Weibull response variable. Additionally, it provides the Bayesian estimate of the discrete Weibull regression model using the random walk Metropolis algorithm. The prior distributions of the coefficient predictors were carried out based on the uniform non-informative, normal and Laplace distributions. The performance of the Bayes estimators was also compared with the maximum likelihood estimator in terms of the mean square error and the coverage probability through the Monte Carlo simulation study. Meanwhile, a real data set was analyzed to show how the proposed model and the methods work in practice.</p></abstract>

Kannat Na Bangchang

<abstract> <p>Typically, in high dimensional data sets, many covariates are not significantly associated with a response. Moreover, those covariates are highly correlated, leading to a multicollinearity problem. Hence, the model is sparse since the coefficient of most covariates are likely to be zero. The classical frequentist or likelihood-based variable selection via any criterion such as Bayesian Information Criteria (BIC) and Akaike Information Criteria (AIC) or a stepwise subset selection becomes infeasible when the number of variables are large. An alternative solution is a Bayesian variable selection. In this study, we used a variable selection via a Bayesian variable selection and the least absolute shrinkage and selection operator (LASSO) method in the logistic regression model. Moreover, those methods were expanded to be applied to real datasets.</p> </abstract>

Luiz Rodrigues, Robson Parmezan Bonidia, Jacques Duílio Brancher

Hong QIN, Na ZOU

Na LI

Jarosław Sołowiej

Ji-Young Na, Soon-Yeong Chung

NA SUN, SHENGJIAN WU

C. Rajski

S. Zubrzycki

E. Pleszczynska

F. Szczotka

Jianhui Huang, Na Li