Template-Type: ReDIF-Article 1.0 Author-Name: S.K. Yadav Author-X-Name-First: S.K. Author-X-Name-Last: Yadav Author-Name: Dinesh K. Sharma Author-X-Name-First: Dinesh K. Author-X-Name-Last: Sharma Author-Name: S.S. Mishra Author-X-Name-First: S.S. Author-X-Name-Last: Mishra Author-Name: Alok Kumar Shukla Author-X-Name-First: Alok Kumar Author-X-Name-Last: Shukla Title: Use of auxiliary variables in searching efficient estimator of population mean Abstract: The paper deals with the improvement in Yadav et al. (2016) estimator of the population mean using the known coefficient of kurtosis and median of the auxiliary variable. The large sample properties of the estimator, bias and mean squared error (MSE), have been calculated up to the first order of approximation. The optimum values of the characterising scalars which minimise the MSE of the proposed estimator have been obtained. A comparative study has been conducted with the existing estimators of the population mean using auxiliary variable under simple random sampling scheme. To justify the improvement of proposed estimator over Yadav et al. (2016) and other estimators of the population mean, an empirical study is also presented by calculating the mean squared errors of the different estimator of the population mean under simple random sampling. Journal: Int. J. of Multivariate Data Analysis Pages: 230-244 Issue: 3 Volume: 1 Year: 2018 Keywords: ratio-cum-product estimator; coefficient of kurtosis; median; bias; mean squared error; MSE; efficiency. File-URL: http://www.inderscience.com/link.php?id=91810 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmda:v:1:y:2018:i:3:p:230-244 Template-Type: ReDIF-Article 1.0 Author-Name: Yeong Nain Chi Author-X-Name-First: Yeong Nain Author-X-Name-Last: Chi Title: Cluster-based multinomial logistic regression analysis of saltwater anglers' concerns of marine environmental threats Abstract: This study utilised cross-sectional data extracted from the 2013 national saltwater angler survey to examine saltwater anglers' concerns to the threats of marine environment, to identify groups exhibiting common patterns of responses, and to examine the association between socio-demographic characteristics and the groups identified. Concerns of marine environmental threats from these participants were examined through factor analysis which identified three reliable factors. Cluster analysis was employed to identify three prominent groups. Statistical tests were employed to investigate the association between socio-demographic characteristics, including age, gender, income level, educational level, region of the respondent, and the saltwater angler groups identified. Results of this study may provide insight regarding the concerns of marine environmental threats from saltwater anglers as an indicator of potential participation and behaviour of saltwater recreational fishing activities. Journal: Int. J. of Multivariate Data Analysis Pages: 245-260 Issue: 3 Volume: 1 Year: 2018 Keywords: saltwater recreational fishing; anglers; marine environmental threats; factor analysis; cluster analysis; multinomial logistic regression analysis. File-URL: http://www.inderscience.com/link.php?id=91827 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmda:v:1:y:2018:i:3:p:245-260 Template-Type: ReDIF-Article 1.0 Author-Name: Luciele Cristina Pelicioni Author-X-Name-First: Luciele Cristina Author-X-Name-Last: Pelicioni Author-Name: Joana Ramos Ribeiro Author-X-Name-First: Joana Ramos Author-X-Name-Last: Ribeiro Author-Name: Rodrigo Arnaldo Scarpel Author-X-Name-First: Rodrigo Arnaldo Author-X-Name-Last: Scarpel Author-Name: Tessaleno Devezas Author-X-Name-First: Tessaleno Author-X-Name-Last: Devezas Author-Name: Mischel Carmen Neyra Belderrain Author-X-Name-First: Mischel Carmen Neyra Author-X-Name-Last: Belderrain Author-Name: Francisco Cristovão Lourenço De Melo Author-X-Name-First: Francisco Cristovão Lourenço De Author-X-Name-Last: Melo Title: Innovation output estimation method for a national innovation system: application to the BRICS countries Abstract: The evaluation of the ability of a country to promote innovation is a valuable step to assist countries in designing suitable economic policies. A usual approach to perform such evaluation and for ranking countries according to their innovation rate is the usage of innovation indices. However, those indices are not appropriate for evaluating whether a set of countries are producing innovation below of what is expected from them. Thus, the aim of this study was to create a model using structural equation model (SEM) to allow evaluating the innovation capacity of any country and to calculate the expected outputs as a function of the estimated inputs. The model showed that the countries belonging to the BRICS group, except for South Africa, presented outputs close to or above the expected for these countries, achieving a performance similar to the group of countries with negative scores of input and output factors. Journal: Int. J. of Multivariate Data Analysis Pages: 261-279 Issue: 3 Volume: 1 Year: 2018 Keywords: BRICS; causal relationships; confirmatory factor analysis; global innovation index; innovation capacity performance; innovation output; innovation index; multiple regression; national innovation system; structural equation modelling; SEM. File-URL: http://www.inderscience.com/link.php?id=91828 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmda:v:1:y:2018:i:3:p:261-279 Template-Type: ReDIF-Article 1.0 Author-Name: Maria Helena Pestana Author-X-Name-First: Maria Helena Author-X-Name-Last: Pestana Author-Name: Margarida Rolland Sobral Author-X-Name-First: Margarida Rolland Author-X-Name-Last: Sobral Title: Alzheimer's disease research: a network science approach Abstract: A limit number of studies have applied bibliometric visualisation to explore the network structure of Alzheimer's disease (AD). This paper uses CiteSpace, Carrot and VOSviewer to analyse and visualise the intellectual structure of AD, characterising, quantitatively and qualitatively, the global scientific outputs and identifying their trends. The 9,753 articles obtained from the science citation index expanded database (SCI-E), from Web-of-Science, were analysed. The publication data is analysed computationally to identify publication patterns, a rate of growth of publications, types of authorship collaboration, the most productive authors, countries, institutions, journals, keywords, the citation and keyword patterns, the hotspots and the areas of research on the AD. The paper presents a detailed analytical mapping of AD research and charts the progress of discipline with various useful parameters. The authors expect to contribute to the theory, supplying researchers with new tools and enabling practitioners to improve their knowledge about the AD evolution and trends. Journal: Int. J. of Multivariate Data Analysis Pages: 201-217 Issue: 3 Volume: 1 Year: 2018 Keywords: bibliometric analysis; scientific outputs; Alzheimer; keywords analysis; collaboration network; research trend. File-URL: http://www.inderscience.com/link.php?id=91838 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmda:v:1:y:2018:i:3:p:201-217 Template-Type: ReDIF-Article 1.0 Author-Name: Ricardo Goulart Serra Author-X-Name-First: Ricardo Goulart Author-X-Name-Last: Serra Title: An application of hierarchical linear models to analyse Brazilian financial betas Abstract: It is common to consider that firms from the same industry share a common unlevered beta. This reasoning implies: 1) a nested structure (firms - level 1 - nested in industries - level 2), 2) that unlevered beta variability is concentrated at industry level. I analysed, through hierarchical linear model, 92 Brazilian firms on 31 December 2016. Unlevered betas were calculated with different criteria in terms of: 1) historical period; 2) periodicity of return; 3) market index proxy. I considered two different industry classifications. Results do not favour the common practice as: 1) not all scenarios result in the desired nested structure as well as; 2) the majority of the variability is at firm level (differences in firms from the same industry). As a comparison, I studied 282 North American firms and encountered nested structure in all scenarios but with large variability among firms from the same industry. Journal: Int. J. of Multivariate Data Analysis Pages: 218-229 Issue: 3 Volume: 1 Year: 2018 Keywords: financial markets; beta; multilevel analysis; HLM; multivariate data analysis; Brazilian firms. File-URL: http://www.inderscience.com/link.php?id=91849 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmda:v:1:y:2018:i:3:p:218-229 Template-Type: ReDIF-Article 1.0 Author-Name: Maxwell Azubuike Ijomah Author-X-Name-First: Maxwell Azubuike Author-X-Name-Last: Ijomah Author-Name: Oyinebifun Emmanuel Biu Author-X-Name-First: Oyinebifun Emmanuel Author-X-Name-Last: Biu Author-Name: Olaide Temitayo Toru Author-X-Name-First: Olaide Temitayo Author-X-Name-Last: Toru Title: Discriminating between first and second order linear and nonlinear models for optimality 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. Journal: Int. J. of Multivariate Data Analysis Pages: 281-307 Issue: 4 Volume: 1 Year: 2018 Keywords: data transformation; optimality criterion; model adequacy criteria; optimising multivariable function; OPM. File-URL: http://www.inderscience.com/link.php?id=96044 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmda:v:1:y:2018:i:4:p:281-307 Template-Type: ReDIF-Article 1.0 Author-Name: Oyinebifun Emmanuel Biu Author-X-Name-First: Oyinebifun Emmanuel Author-X-Name-Last: Biu Author-Name: Iheanyi Sylvester Iwueze Author-X-Name-First: Iheanyi Sylvester Author-X-Name-Last: Iwueze Title: On the adequacy of the polynomial approximation to the exponential growth curve model 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. Journal: Int. J. of Multivariate Data Analysis Pages: 308-326 Issue: 4 Volume: 1 Year: 2018 Keywords: trending curves; exponential growth curve; polynomial growth curve; absolute error; paired observation test. File-URL: http://www.inderscience.com/link.php?id=96046 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmda:v:1:y:2018:i:4:p:308-326 Template-Type: ReDIF-Article 1.0 Author-Name: Adilson Da Silva Author-X-Name-First: Adilson Da Author-X-Name-Last: Silva Author-Name: Miguel Fonseca Author-X-Name-First: Miguel Author-X-Name-Last: Fonseca Title: The method sub-d for variance components estimation in random one-way designs 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. Journal: Int. J. of Multivariate Data Analysis Pages: 327-336 Issue: 4 Volume: 1 Year: 2018 Keywords: orthogonal matrix; sub-d; one-way designs; variance components. File-URL: http://www.inderscience.com/link.php?id=96051 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmda:v:1:y:2018:i:4:p:327-336 Template-Type: ReDIF-Article 1.0 Author-Name: S.K. Yadav Author-X-Name-First: S.K. Author-X-Name-Last: Yadav Author-Name: Dinesh K. Sharma Author-X-Name-First: Dinesh K. Author-X-Name-Last: Sharma Author-Name: S.S. Mishra Author-X-Name-First: S.S. Author-X-Name-Last: Mishra Title: New enhanced class of estimators of population mean using known median of study variable 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. Journal: Int. J. of Multivariate Data Analysis Pages: 337-347 Issue: 4 Volume: 1 Year: 2018 Keywords: study variable; auxiliary variable; median; bias; mean squared error; MSE; percentage relative efficiency. File-URL: http://www.inderscience.com/link.php?id=96061 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmda:v:1:y:2018:i:4:p:337-347 Template-Type: ReDIF-Article 1.0 Author-Name: Maria Helena Pestana Author-X-Name-First: Maria Helena Author-X-Name-Last: Pestana Author-Name: Wan-Chen Wang Author-X-Name-First: Wan-Chen Author-X-Name-Last: Wang Author-Name: Luiz Abel Moutinho Author-X-Name-First: Luiz Abel Author-X-Name-Last: Moutinho Title: Global affective computing research in the period 1997-2017: a bibliometric analysis 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. Journal: Int. J. of Multivariate Data Analysis Pages: 348-370 Issue: 4 Volume: 1 Year: 2018 Keywords: affective computing; bibliometric analysis; scientific outputs; collaboration network; research hotpots; research trends. File-URL: http://www.inderscience.com/link.php?id=96076 File-Format: text/html File-Restriction: Access to full text is restricted to subscribers. Handle: RePEc:ids:ijmda:v:1:y:2018:i:4:p:348-370