JournalTOCs

International Journal of Multivariate Data Analysis
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Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2396-8303 - ISSN (Online) 2396-8311
Published by Inderscience Publishers

[439 journals]

Discriminating between first and second order linear and nonlinear models
for optimality
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  Authors: Maxwell Azubuike Ijomah, Oyinebifun Emmanuel Biu, Olaide Temitayo Toru
  Pages: 281 - 307
  Abstract: In this paper, an examination of the relationship between a response variable and several explanatory variables was considered for first and second order regression models (with and without interaction). To achieve this, the behaviour of the controllable variables (i.e., reaction time, reaction temperature and moisture content) against response variable (drying rate of bush mango seeds) was examined using ordinary least square method with the aid of Microsoft Excel and Minitab 16. Furthermore, the comparison of the fitted models, using model adequacy criteria procedure and optimality criterion technique was also done. This was to determine the most suitable model that best predicts optimal response variable for given settings of the controllable variables. The result showed that the second order regression model with interaction was the most suitable model, and a new operating region in which a process or product may be improved was identified using optimising multivariable function. This research recommends the extreme points and the identified optimal value for production process.
  Keywords: data transformation; optimality criterion; model adequacy criteria; optimising multivariable function; OPM
  Citation: International Journal of Multivariate Data Analysis, Vol. 1, No. 4 (2018) pp. 281 - 307
  PubDate: 2018-11-09T23:20:50-05:00
  DOI: 10.1504/IJMDA.2018.096044
  Issue No: Vol. 1, No. 4 (2018)
On the adequacy of the polynomial approximation to the exponential growth
curve model
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  Authors: Oyinebifun Emmanuel Biu, Iheanyi Sylvester Iwueze
  Pages: 308 - 326
  Abstract: Exponential growth curves are usually approximated with a finite order polynomial curve in the study of trending curves. This is done because the most popular way of removing the trend component is by differencing. This paper shows that the trend curve cannot be removed by differencing when the trend curve is the exponential growth curve. Exponential curves are transcendental functions which can be reduced to a finite order polynomial by Maclaurin series expansion. The objective of this paper is to examine the adequacy of the polynomial approximation of the exponential growth curve with respect to its growth rate and sample size. The coefficients of the associated polynomial curve were obtained theoretically by the use of Maclaurin series expansion method. Next, exponential growth curves with varying growth rates and sample sizes were simulated. Adequate polynomials were fitted to the simulated exponential growth curves and the coefficients obtained were compared with the theoretical coefficients using absolute error and paired tests. Results obtained show that adequacy depend on both growth rate and sample size. For the purpose of statistical analysis, the highest sample size of 28 is not useful, especially in times series analysis where the demand of samples of 60 or more is made.
  Keywords: trending curves; exponential growth curve; polynomial growth curve; absolute error; paired observation test
  Citation: International Journal of Multivariate Data Analysis, Vol. 1, No. 4 (2018) pp. 308 - 326
  PubDate: 2018-11-09T23:20:50-05:00
  DOI: 10.1504/IJMDA.2018.096046
  Issue No: Vol. 1, No. 4 (2018)
The method sub-d for variance components estimation in random
one-way designs
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  Authors: Adilson Da Silva, Miguel Fonseca
  Pages: 327 - 336
  Abstract: This paper approachs the new estimator for variance components in mixed linear models with an arbitrary number of variance components, called <i>sub-d</i>. This estimator was deduced and tested in random 'one-way' and 'two-way' nested and crossed designs with balanced or unbalanced data, by Silva (2017); specifically, this paper aims to give the sub-d explicit formula for the two variance components in random 'one-way' designs, ensuring their existence through consistent theoretical results. In order to derive the explicit above announced formula, we propose and prove some robust algebraic results. A numerical example where both variance components are estimated is given.
  Keywords: orthogonal matrix; sub-d; one-way designs; variance components
  Citation: International Journal of Multivariate Data Analysis, Vol. 1, No. 4 (2018) pp. 327 - 336
  PubDate: 2018-11-09T23:20:50-05:00
  DOI: 10.1504/IJMDA.2018.096051
  Issue No: Vol. 1, No. 4 (2018)
New enhanced class of estimators of population mean using known median of
study variable
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  Authors: S.K. Yadav, Dinesh K. Sharma, S.S. Mishra
  Pages: 337 - 347
  Abstract: The present paper estimates the population mean of the variable under study by improving the class of estimators utilising the known information of the population median of the study variable. The sampling properties of the proposed class of estimators have been studied. In sampling properties, bias and mean squared errors (MSE) have been obtained. This is because a characterising scalar is involved in the estimator and it takes different values. Thus, the optimum value of this characterising constant which minimises the MSE of proposed class has also been obtained. The least value of the MSE of the proposed estimator is obtained for the optimal value of characterising constant. The proposed estimator has been compared with the competing estimators for various natural populations under simple random sampling scheme. The conditions under which, a proposed estimator performs better than above estimators have been given. The numerical study shows that the proposed estimator performs better than the competing estimators.
  Keywords: study variable; auxiliary variable; median; bias; mean squared error; MSE; percentage relative efficiency
  Citation: International Journal of Multivariate Data Analysis, Vol. 1, No. 4 (2018) pp. 337 - 347
  PubDate: 2018-11-09T23:20:50-05:00
  DOI: 10.1504/IJMDA.2018.096061
  Issue No: Vol. 1, No. 4 (2018)
Global affective computing research in the period 1997-2017: a
bibliometric analysis
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  Authors: Maria Helena Pestana, Wan-Chen Wang, Luiz Abel Moutinho
  Pages: 348 - 370
  Abstract: Notable fallouts in marketing and financial market prediction have raised the interest by the scientific community and the business world in affective computing (AfC). Automatically recognising and responding to a user's affective states, AfC shows a great potential to improve companies capabilities of customer relationship management. The aim of this study is to evaluate this field of research during the last 20 years, identifying for one side its evolution, by the major publications, citations, journals, authors, productive countries, productive institutions, and collaboration patterns; and for another side, identifying its trends through the analysis of research hotspots, burst keywords, and areas of research done so far. This bibliometric analysis is based on the science citation index expanded (SCI-E), from the Institute of Scientific Information Web-of-Science, which is now firmly established as an integral part of research evaluation methodology especially within the scientific and applied fields. The results show a significant 4.19 rate of growth in AfC, doubling the number of publications in 4.02 years time. This field of interest is paving the way for creativity and innovation, and provides opportunities for its greater development.
  Keywords: affective computing; bibliometric analysis; scientific outputs; collaboration network; research hotpots; research trends
  Citation: International Journal of Multivariate Data Analysis, Vol. 1, No. 4 (2018) pp. 348 - 370
  PubDate: 2018-11-09T23:20:50-05:00
  DOI: 10.1504/IJMDA.2018.096076
  Issue No: Vol. 1, No. 4 (2018)