International Journal of Multivariate Data Analysis (8 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.
PLS-SEM or CB-SEM: updated guidelines on which method to use
by Joe F. Hair Jr., Lucy M. Matthews, Ryan L. Matthews, Marko Sarstedt
Abstract: Numerous statistical methods are available for social researchers. Therefore, knowing the appropriate technique can be a challenge. For example, when considering structural equation modelling (SEM), selecting between covariance-based (CB-SEM) and variance-based partial least squares (PLS-SEM) can be challenging. This paper applies the same theoretical measurement and structural models and dataset to conduct a direct comparison. The findings reveal that when using CB-SEM, many indicators are removed to achieve acceptable goodness-of-fit, when compared to PLS-SEM. Also, composite reliability and convergent validity were typically higher using PLS-SEM, but other metrics such as discriminant validity and beta coefficients are comparable. Finally, when comparing variance explained in the dependent variable indicators, PLS-SEM was substantially better than CB-SEM. Updated guidelines assist researchers in determining whether CB-SEM or PLS-SEM is the most appropriate method to use.
Keywords: structural equation modelling; SEM; PLS-SEM; CB-SEM.
Multivariate autoregressive model for ECG signal forecasting
by Sarita Kansal, Prashant P. Bansod, Abhay Kumar
Abstract: In this paper, multivariate autoregressive modelling is used to analyse the correlation between diagnostic components of an ECG signal. The value of diagnostic components is identified in every beat, and is measured by wavelet transform. The diagnostic components are considered as ECG variables for modelling and it represents the time series signals. The forecasting of ECG variable 'IHR' is evaluated by using multivariate autoregressive model. The model is characterised by different number of ECG variables and past values of each variable. It affects the forecasting accuracy, which is measured by mean absolute error (MAE). The results show that as the number of diagnostic components is increasing in terms of ECG variables, the forecasting accuracy is enhanced by reduction in the value of MAE. The forecasting accuracy is calculated for the forecasting horizon of 80 ECG beats.
Keywords: multivariate autoregressive model; mean absolute error; MAE; ECG variables; AR model.
The mediating role of the dimensions of attitude towards advertisement and brand attitude on purchase intention
by Sridhar Manohar
Abstract: The study examines: 1) reciprocal relationship hypothesis between dimensions of attitude towards advertisement and brand attitude, and how do they influence the customer purchase intention; 2) how the dimensions of Aad and Abr act as a mediator in predicting the PI of a ready-to-eat food product. Based on non-probability convenience sampling, 300 customers were selected from Chennai, capital of state Tamil Nadu in India. The result indicates there is a reciprocal relationship between Abr and Aad and the pathway from Aad through the mediator: 1) Abr predicts PI better than the second pathway that is from Abr through the mediator; 2) Aad on predicting PI.
Keywords: brand attitude; dimensions of attitude towards advertisement; mediating effect; structural equation modeling; reciprocal relationship; visual PLS; multivariate regression.
What is better: to be roughly right or exactly wrong? The role of quantitative methods in financial accounting
by Eduardo Flores, Guillermo Oscar Braunbeck
Abstract: The aim of this commentary is to discuss whether quantitative techniques are relevant for the development of financial accounting practice. We focus on the use of quantitative techniques in financial accounting practice rather than financial accounting research because since Ball and Brown (1968) and Beaver (1968), the association with academic research has already received considerable attention and produced a broad range of manuscripts. Our focal point is particularly within the rules of International Financial Reporting Standards (IFRSs), which allow or request the fair value measurement. Therefore, we aim to bridge this concept with quantitative techniques. We conclude that the use of quantitative methods in financial accounting is closely related to the development and use of fair value measurement in financial reports. Last but not least, some examples are given of how quantitative methods can improve the success of financial reports in the future.
Keywords: financial accounting; fair value; quantitative methods; accounting measurement; accounting informational relevance.
A mixed integer linear programming model for the multi-item uncapacitated lot-sizing problem: a case study in the trailer manufacturing industry
by Maryam Mohammadi, Ehsan Shekarian
Abstract: In this paper, a mixed integer linear programming model for the multi-item uncapacitated lot-sizing problem is presented. The considered factors for formulating the proposed model are the monthly demand of the selected product from the case study company, type of parts used in the product and their consumption coefficients based on the bill of materials, lead time to receive parts from outsourcing suppliers, the costs of ordering, purchasing, and holding, and the amount of safety stock for each part. Accordingly, several forecasting techniques are tried to determine future demands. The prices of the selected parts are estimated using linear regression method. The optimal safety stock for each part is calculated based on variance in demand, lead time and the target service level. Material requirements planning are also performed to obtain the economic purchasing schedule of parts. LINGO and GAMS software are used to solve and validate the suggested model respectively. The results show that the proposed model can find the optimum order quantity for each part per period, which minimises the total cost.
Keywords: mixed integer programming; uncapacitated lot-sizing; demand forecasting; price estimation; safety stock; material requirements planning; MRP; optimum order quantity; cost minimisation; LINGO; GAMS; trailer manufacturer.