ISSN 1842-4562
Member of DOAJ
Journal Home > Volume 9, Issue 2 - June 30, 2014

JAQM Volume 9, Issue 2 - June 30, 2014


Toward Fits to Scaling-Like Data, but with Inflection Points &Generalized Lavalette Function (p )

Experimental and empirical data are often analyzed on log-log plots in order to find some scaling argument for the observed/examined phenomenon at hands, in particular for rank-size rule research, but also in critical phenomena in thermodynamics, and in fractal geometry. The fit to a straight line on such plots is not always satisfactory. Deviations occur at low, intermediate and high regimes along the log(x)-axis. Several improvements of the mere power law fit are discussed, in particular through a Mandelbrot trick at low rank and a Lavalette power law cut-off at high rank. In so doing, the number of free parameters increases. Their meaning is discussed, up to the 5 parameter free super-generalized Lavalette law and the 7-parameter free hyper-generealized Lavalette law. It is emphasized that the interest of the basic 2-parameter free Lavalette law and the subsequent generalizations resides in its "noid" (or sigmoid, depending on the sign of the exponents) form on a semi-log plot; something incapable to be found in other empirical law, like the Zipf-Pareto-Mandelbrot law. It remained for completeness to invent a simple law showing an inflection point on a log-log plot. Such a law can result from a transformation of the Lavalette law through x → log(x), but this meaning is theoretically unclear. However, a simple linear combination of two basic Lavalette law is shown to provide the requested feature. Generalizations taking into account two super-generalized or hyper-generealized Lavalette laws are suggested, but need to be fully considered at fit time on appropriate data.

A SVAR Analysis of the Relationship between Romanian Unemployment Rates and the Size of the Shadow Economy (p )
Adriana AnaMaria DAVIDESCU, Ion DOBRE

The paper analyses the relationship between shadow economy and unemployment rates using a Structural VAR approach for quarterly data during the period 2000-2010. The size of Romanian shadow economy is estimated using the currency demand approach based on VECM models, stating that its size is decreasing over the analyzed period, from 36.5% at the end of 2000 to about 31.5% of real GDP at the middle of 2010. The relationship between the variables is tested by imposing a long-run restriction in the Structural VAR model to analyze the impact of the shadow economy to a temporary shock in unemployment. The accumulated responses generated by a positive supply shock (unemployment rate) confirms that in the short-run, a rise in both registered and ILO unemployment rates in formal sector will lead to a decrease in the number of people who work in the shadow economy in the second quarter following the initial shock and to an smaller increase in the size of the Romanian shadow economy in the third quarter following the initial shock.

Intuitionistic Fuzzy Multicriteria Group Decision-Making Approach to Quality Clay-Brick Selection Problems Based on Grey Relational Analysis (p )
Kalyan MONDAL, Surapati PRAMANIK

This paper presents quality Clay-Brick selection process based on intuitionistic fuzzy multi criteria group decision making through grey relational analysis. Brick plays an important role in construction field. Intuitionistic fuzzy weighted averaging operator is used to aggregate individual opinions of decision makers into a group opinion. Six criteria are considered for selection process and the criteria are obtained from expert opinions. The criteria are namely solidity, color, size and shape, strength of Brick, cost of Bricks and carrying cost. Weights of the criteria are obtained from domain experts by using a questionnaire. The rating of an alternative with respect to certain criterion offered by decision maker is represented by linguistic variable that can be expressed by intuitionistic fuzzy sets. An intuitionistic fuzzy set, which is characterized by membership function (degree of acceptance), non-membership function (degree of rejection) and the degree of indeterminacy or the degree of hesitancy, is a more general and suitable way to deal with imprecise information. Grey relational analysis is used for ranking and selection of alternatives. An illustrative numerical example for quality Brick selection is solved to show the effectiveness of the proposed model.

Oil Seeds Area and Production Variability in Bangladesh (p )
Muhammad Abdul Baker CHOWDHURY, M. Taj UDDINB, M Jamal UDDINL

Oil seed is one of the most important sources of vegetable oil. It plays a vital role in agricultural sectors of Bangladesh. However, the production of oilseed cannot meet up its annual demand of Bangladesh. The objective of this study was to measure the change and instability of oil seeds in Bangladesh in the context of area, production, and yields. Data were extracted from the statistical year books of Bangladesh Bureau of Statistics (BBS) and the study period was 1987 to 2010. Our analysis revealed that the production and yield of oil seeds were increased sharply though the cultivable areas were decreased. The growth rate in production and yield of oil seeds were satisfactory over the study period although they were not stable. Moreover, the results showed that it is not sufficient to fulfil the present demand of vegetable oil in Bangladesh. We recommend policy makers and stakeholders can give due attention to improve this sector for the betterment of the vegetable sector sustainable crop security in Bangladesh.

Quantitative Risk Management Techniques using Interval Analysis, with Applications to Finance and Insurance (p )
Silvia DEDU, Florentin SERBAN, Ana TUDORACHE

In this paper we study some risk management techniques using optimization problems under uncertainty. In decision making problems under uncertainty, the parameters of the models used can not be exactly described by real numbers, because of the imprecision of the data. In order to overcome this drawback the uncertainty of the parameters can be modeled by using interval numbers and interval random variables. Concepts of interval analysis are introduced in this article. Computational results are provided.