Siana Halim

Statistical analysis on the intellectual capital statement

Purpose – The intellectual capital (IC) can be divided into three categories, i.e. human, structural, and relationship capitals. The purpose of this paper is to investigate the correlation among those capitals to their indicators, particularly for intellectual capital statement made in Germany and intellectual capital statement made in Europe models.Design/methodology/approach – In these two models, each capital has four, six, and five indicators, respectively. So totally, there are 15 indicators. Structural equation modeling and its sensitivity analysis are utilized for measuring the correlation among those capitals to their indicators.Findings – Among those 15 indicators, 14 indicators have strong correlation with their respective capitals. Moreover, there exist strong correlation in a similar weight among those capitals, i.e. the correlations between human (HC) and structural capital (SC) is 0.88, SC and relationship capital (RC) is 0.87 and HC to RC is 0.81.Originality/value – So far, the data collect...

Wild Bootstrap Tests

In this article, we have derived tests based on the wild bootstrap that allow checking for differences between two irregular signals observed with additive noise where the latter consists of independent but not necessarily identically distributed random variables. The signals are first denoised by kernel estimates and then compared by looking at the integrated squared difference. The bound between accepting and rejecting the hypothesis of equal signals are determined by the wild bootstrap and numerically calculated by Monte Carlo simulation. The test is applied to all pairwise rows and columns of two images, which results in an algorithm that allows detection of defects and additional information on their location and shape surface inspection problems. The idea and theory of the test may be straightforwardly extended to the direct comparison of two images. This is computationally less expensive than doing all the row- and columnwise tests, but it provides less information.

The Effect of Operation 24 Hours on Reducing Collision in the City of Edmonton

In the City of Edmonton, in order to reduce the prevalence of collisions, the Operation 24 Hours program (OPS24) was developed by using existing police and transportation services resources. The program uses traditional manned police speed enforcement method, which are supplemented by traffic safety messages displayed on permanent and mobile dynamic messaging signs (DMS). In this paper, collision data analysis was performed by looking at the daily number of collisions from 2008 to 2011 that covers 28 Operation 24 Hours (OPS24) events. The objective of the collision data analysis is to analyze if there is a reduction in collision frequencies after OPS24 was held and examined how long the collision reduction effect last. Weather factors such as temperature, thickness of snow, and wind gust have been considered by many as a great influence on collision occurrences, especially in a city with long and cold winter such as Edmonton. Therefore, collision modeling was performed by considering these external weather factors. To analyze the linear and periodic trend of different collision types (injury, fatal, and property damage only (PDO)) and examine the influence of weather factors on collisions, negative binomial time series model that accounts for seasonality and weather factors was used to model daily collision data. The modeling also considered collision proportion to account for missing traffic volume data; the Gaussian time series model that accounts for seasonality and weather factors was used to model collision proportion. To estimate the collision trend and test for changes in collision levels before/after OPS24, interrupted time series model with segmented regression was used. While for estimating how long the effect of the OPS24 last, change point method was applied.

Pemetaan Penderita Pneumonia di Surabaya dengan Menggunakan Geostatistik

Bayesian Belief Network (BBN), one of the data mining classification methods, is used in this research for mining and analyzing medical track record from a relational data table. In this paper, a mutual information concept is extended using fuzzy labels for determining the relation between two fuzzy nodes. The highest fuzzy information gain is used for mining fuzzy association rules in order to extend a BBN. Meaningful fuzzy labels can be defined for each domain data. For example, fuzzy labels of secondary disease and complication disease are defined for a disease classification. The implemented of the extended BBN in a application program gives a contribution for analyzing medical track record based on BBN graph and conditional probability tables.

Parameter estimation of space-time model using genetic algorithm

The Space-Time Autoregressive Moving-Average (STARMA) model family is a statistical inductive model that can be used to describe stationary (or weak stationary) space-time processes. However, parameter estimation of the model often is not easy to obtain analytically because of the hard computation or the unknown probability density function underlying the data. To ease the difficulty, an approach to estimate the parameter is proposed in this study, i.e. genetic algorithm (GA). GA is one of the meta-heuristic methods widely used in many applications including the parameter estimation. The GA is performed through simulations of various combinations of selection and crossover parameter chromosomes. The estimation, then, was carried out by the help of freeware R. The performance of the GA in estimating parameter is measured in the sense of the minimum residual sum of squares and the Akaike Information Criterion (AIC). In order to have a comparable solution, we employed the STARMA model of assault arrests in 14 districts of Northeast Boston (1969–1974) of Pfeifer and Deutsch. The results show that the performance of the GA is relatively competitive to the classical method. Since GA is simple to apply, it might be considered as one of the alternative methods for estimating space-time model parameters.

Structural Adaptive Smoothing Procedures

An important problem in image and signal analysis is denoising. Given data y j at locations x j , j = 1, ..., N, in space or time, the goal is to recover the original image or signal m j , j = 1, ..., N, from the noisy observations y j , j = 1, ..., N. Denoising is a special case of a function estimation problem: If m j = m(x j ) for some function m(x), we may model the data y j as real-valued random variables Y j satisfying the regression relation

Group decision using analytical hierarchical process: Surabaya's universities library in digital natives perspective

Y_j = m\left( {x_j } \right) + \varepsilon _j , j = 1,...,N,

Adjusted clustering Clarke-Wright Saving Algorithm for two depots-N vehicles

(6.1) where the additive noise e j , j = 1, ..., N, is independent, identically distributed (i.i.d.) with mean \( \mathbb{E} \) ej = 0. The original denoising problem is solved by finding an estimate \( \hat m\left( x \right) \) of the regression function m(x) on some subset containing all the x j . More generally, we may allow the function arguments to be random variables X j e ℝd themselves ending up with a regression model with stochastic design