International Journal of Multivariate Data Analysis (7 papers in press)
USE OF AUXILIARY VARIABLES IN SEARCHING EFFICIENT ESTIMATOR OF POPULATION MEAN
by Subhash Yadav, Dinesh Sharma, S.S. Mishra, Alok Shukla
Abstract: The paper deals with the improvement in Yadav et al. (2016) estimator of the population mean using 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 characterizing scalars which minimize the MSE of the proposed estimator have been obtained. A comparative study has been conducted with the existing estimators of 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.
Keywords: Ratio-cum-product estimator; coefficient of kurtosis; median; bias; MSE; Efficiency.
Cluster-Based Multinomial Logistic Regression Analysis of Saltwater Anglers Concerns of Marine Environmental Threats
by Yeong Nain Chi
Abstract: This study utilized 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 behavior of saltwater recreational fishing activities.
Keywords: Saltwater Recreational Fishing; Anglers; Marine Environmental Threats; Factor Analysis; Cluster Analysis; Multinomial Logistic Regression Analysis.
Innovation output estimation method for a national innovation system: Application to the BRICS countries
by Luciele Cristina Pelicioni, Joana Ramos Ribeiro, Rodrigo Arnaldo Scarpel, Tessaleno Devezas, Mischel Carmen Neyra Belderrain, Francisco Cristovão Lourenço De Melo
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.
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.
by Maria Helena Pestana, Margarida Rolland Sobral
Abstract: A limit number of studies have applied bibliometric visualisation to explore the network structure of Alzheimers Disease (AD). This paper uses CiteSpace, Carrot and VOSviewer to analyse and visualise the intellectual structure of AD, characterizing, quantitatively and qualitatively, the global scientific outputs, and identifying their trends. The 9,757 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.
Keywords: Bibliometric analysis; Scientific outputs; Alzheimer; Keywords analysis; Collaboration network; Research trend.
An Application of Hierarchical Linear Models to Analyze Brazilian Financial Betas
by Ricardo Goulart Serra
Abstract: It is common to consider that firms from de same industry share a common unlevered beta. This reasoning implies (i) a nested structure (firms level 1 nested in industries level 2) and (ii) that unlevered beta variability is concentrated at industry level. I analyzed, through hierarchical linear model, 92 Brazilian firms on December 31, 2016. Unlevered betas were calculated with different criteria in terms of (i) historical period, (ii) periodicity of return and (ii) market index proxy. I considered 2 different industry classification. Results do not favor 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.
Keywords: financial markets; beta; multilevel analysis; HLM; Brazilian firms.
Discriminating between first and second order linear and non linear models for optimality
by Ijomah Maxwell Azubuike, Oyinebifun Emmanuel Biu, Toru Temitayo Olaide
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 optimizing multivariable function. This research recommends the extreme points and the identified optimal value for production process.
Keywords: Data transformation; optimality criterion; model adequacy criteria and optimizing multivariable function.
On the adequacy of the polynomial approximation to the exponential growth curve model
by Oyinebifun Emmanuel Biu, Iheanyi Sylvester Iwueze
Abstract: Exponential growth curves are usually approximated with a finite order polynomial curve in the study of trending curves in many areas of Statistics. This is done because the most popular way of removing the trend component is by differencing. Exponential growth curves are usually approximated with a finite order polynomial curve in the study of trending curves in many areas of Statistics. This is done because the most popular way of removing the trend component is by differencing. This paper first, 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 Taylor series expansion or its equivalence evaluated at the origin: the Maclaurin series expansion. The main 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.