Dedi Rosadi

Probabilistic soft sets and dual probabilistic soft sets in decision-making

Since its introduction by Molodstov (Computers & Mathematics with Applications 37(4):19–31 1999), soft set theory has been widely applied in various fields of study. Soft set theory has also been combined with other theories like fuzzy sets theory, rough sets theory, and probability theory. The combination of soft sets and probability theory generates probabilistic soft set theory. However, decision-making based on the probabilistic soft set theory has not been discussed in the literature. In this paper, we propose new algorithms for decision-making based on the probabilistic soft set theory. An example to show the application of these algorithms is given, and its possible extensions and reinterpretations are discussed. Inspired by realistic situations, the notion of dual probabilistic soft sets is proposed, and also, its application in decision-making is investigated.

N-soft sets and their decision making algorithms

In this paper, we motivate and introduce the concept of N-soft set as an extended soft set model. Some useful algebraic definitions and properties are given. We cite real examples that prove that N-soft sets are a cogent model for binary and non-binary evaluations in numerous kinds of decision making problems. Finally, we propose decision making procedures for N-soft sets.

Order identification for Gaussian moving averages using the codifference function

In the traditional Box–Jenkins modelling procedure, we use the sample autocorrelation function as a tool for identifying the plausible models for empirical data. In this paper, we consider the sample normalized codifference as a new tool for the preliminary order identification of pure univariate Gaussian moving average process. From simulation studies, we find that when the number of observations are large (more than 100), the proposed method may be superior than a similar identification procedure which is based on the sample autocorrelation function. Simulation results also indicate that the proposed method may perform as well as Gallagher’s procedure [Gallagher, C., 2002, Order identifi-cation for Gaussian moving averages using the Covariation. Journal of the Statistical Computation and Simulations, 72(4), 279–283.].

Generalized Covariation Function for Random Variables with Finite First Moments

Abstract In this paper, we consider a linear dependence measure between two random variables with finite first moments called as the generalized covariation function. The latter includes the covariation and covariance functions as special cases. We investigate some basic properties of the function and define its moment type estimator. We also investigate the numerical properties of this estimator using the simulated SαS data. We show importance of results in applications, we apply the generalized covariation function to estimate the coefficient “beta” of “stable” CAPM (Belkacem et al. [1]) using real data from Indonesian stock exchange (IDX).

The Adequateness of Wavelet Based Model for Time Series

In general, time series is modeled as summation of known information i.e. historical information components, and unknown information i.e. random component. In wavelet based model, time series is represented as linear model of wavelet coefficients. Wavelet based model captures the time series feature perfectly when the historical information components dominate the process. In other hand, it has low enforcement when the random component dominates the process. This paper proposes an effort to develop the adequateness of wavelet based model, when the random component dominated the process. By weighted summation, the data is carried to the new form which has higher dependencies. Consequently, wavelet based model will work better. Finally, it is hoped that the better prediction of wavelet based model will be carried to the original prediction in reverting process.

New procedure for determining order of subset autoregressive integrated moving average (ARIMA) based on over-fitting concept

One of the most popular models that usually be used to predict time series data is Autoregressive Integrated Moving Average (ARIMA) model. The most crucial steps in ARIMA modeling are identification and selection the best model based on available data. These steps require a good understanding about the characteristics of the process in terms of their theoretical autocorrelation function (ACF) and partial autocorrelation function (PACF). In identification step, the goal is to match the patterns of the sample ACF and PACF with the patterns of theoretical ACF and PACF for determining an appropriate order of ARIMA, including order of subset ARIMA. In this paper, we propose the new procedure for determining the order of ARIMA based on over-fitting concept. The process is started from the simplest ARIMA model that all of parameters are statistically significant and determination of an additional order AR or MA is based on over-fitting concept, i.e. based on ACF of the residual model. This new proposed procedure is applied for constructing a subset ARIMA model of Indonesias inflation data. The results show that the proposed procedure yields an appropriate order of subset ARIMA model for Indonesias inflation data.

A social choice approach to graded soft sets

We establish a correspondence between ideas from soft computing and social choice. This connection permits to draw bridges between choice mechanisms in both frameworks. We prove that both Soft sets and the novel concept of Graded soft sets can be faithfully represented by well-established voting situations in Social Choice. To be precise, their decision making mechanism by choice values coincides with approval voting and the Borda rule respectively. This analysis lays the basis for new insights into soft-set-inspired decision making with a social choice foundation.

One period coupon bond valuation with revised first passage time approach and the application in Indonesian corporate bond

The value of a corporate bond is conventionally expressed in terms of zero coupon bond. In practice, the most common form of debt instrument is coupon bond and allows early default before maturity as safety covenant for the bondholder. This paper study valuation for one period coupon bond, a coupon bond that only give one time coupon at the bond period. It assumes that the model give bondholder the right to reorganize a firm if its value falls below a given barrier. Revised first passage time approach is applied for default time rule. As a result, formulas of equity, liability, and probability of default is derived for this specified model. Straightforward integration under risk neutral pricing is used for deriving those formulas. For the application, bond of Bank Rakyat Indonesia (BRI) as one of the largest bank in Indonesia is analyzed. R computing show that value of the equity is IDR 453.724.549.000.000, the liability is IDR 2.657.394.000.000, and the probability if default is 5.645305E-47 %.

A Second Correlation Method for Multivariate Exchange Rates Forecasting

Foreign exchange market is one of the most complex dynamic market with high volatility, non linear and irregularity. As the globalization spread to the world, exchange rates forecasting become more important and complicated. Many external factors influence its volatility. To forecast the exchange rates, those external variables can be used and usually chosen based on the correlation to the predicted variable. A new second correlation method to improve forecasting accuracy is proposed. The second correlation is used to choose the external variable with different time interval. The proposed method is tested using six major monthly exchange rates with Nonlinear Autoregressive with eXogenous input (NARX) compared with Nonlinear Autoregressive (NAR) for model benchmarking. We evaluated the forecasting accuracy of proposed method is increasing by 16.8% compared to univariate NAR model and slight better than linear correlation on average for Dstat parameter and gives almost no improvement for MSE.

Analysis of Palm Oil Production, Export, and Government Consumption to Gross Domestic Product of Five Districts in West Kalimantan by Panel Regression

Gross Domestic Product (GDP) is an indicator of economic growth in a region. GDP is a panel data, which consists of cross-section and time series data. Meanwhile, panel regression is a tool which can be utilised to analyse panel data. There are three models in panel regression, namely Common Effect Model (CEM), Fixed Effect Model (FEM) and Random Effect Model (REM). The models will be chosen based on results of Chow Test, Hausman Test and Lagrange Multiplier Test. This research analyses palm oil about production, export, and government consumption to five district GDP are in West Kalimantan, namely Sanggau, Sintang, Sambas, Ketapang and Bengkayang by panel regression. Based on the results of analyses, it concluded that REM, which adjusted-determination-coefficient is 0,823, is the best model in this case. Also, according to the result, only Export and Government Consumption that influence GDP of the districts.