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Media Statistika
Published by Universitas Diponegoro
ISSN : -     EISSN : 24770647     DOI : -
Core Subject : Science,
Decision Sciences, Operations Research & Management
Arjuna Subject : -
Articles 271 Documents
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APPLICATION OF DELTA GAMMA (THETA) NORMAL APPROXIMATION IN RISK MEASUREMENT OF AAPL'S AND GOLD'S OPTION Sulistianingsih, Evy; Martha, Shantika; Andani, Wirda; Umiati, Wiji; Astuti, Ayu
MEDIA STATISTIKA Vol 16, No 2 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.2.160-169

Abstract

The option value has a nonlinear dependence relationship on risk factors existing in the capital market. Therefore, this paper considered utilizing Delta Gamma (Theta) Normal Approximation (DGTNA) as a nonlinear approach to determine the change of profit/loss of a European call option to assess the option risk. The method uses the second order of Taylor Polynomial around the stock price underlying the option to approximate the option profit/loss, which is crucial to construct the VaR based on DGTNA. VaR based on DGTNA also considered three Greeks, namely Delta, Gamma, and Theta, known as sensitivity measures in option. This research applied VaR based on DGTN approximation to analyze the European call option of Apple Inc (AAPL) and Barrick Gold Corporation (GOLD) for several strike prices. The performance of DGTN VaR analyzed by Kupiec Backtesting summarized that in this case, DGTN VaR provides the best risk assessment over different confidence levels (80, 90, 95, and 99 percent) compared to Delta Normal VaR and Delta Gamma Normal VaR.
BREAST CANCER CLASSIFICATION USING SUPPORT VECTOR MACHINE (SVM) AND LIGHT GRADIENT BOOSTING MACHINE (LIGHTGBM) MODELS Kartikasari, Puspita; Utami, Iut Tri; Suparti, Suparti; Rahman, Syair Dafiq Faizur
MEDIA STATISTIKA Vol 16, No 2 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.2.182-193

Abstract

This study examines the existence of breast cancer from the perspective of statistics as one alternative solution. From a statistical point of view, breast cancer management can be done with early detection and appropriate and fast treatment measures through diagnosis classification. In conducting early detection, an accurate diagnosis model is needed and can be developed by developing and testing statistical methods, one of which is the classification method. The classification methods used in this study are Support Vector Machine (SVM) and LightGBM. Both methods have a high level of classification accuracy because the algorithm used is robust and sensitive in determining each object in the classification member. Therefore, these two methods classify breast cancer into malignant and benign categories. The results of this study show that the best method to classify breast cancer is the SVM method, with an accuracy rate of 97.9%.
SURVIVAL ANALYSIS FOR RECURRENT EVENT DATA USING COUNTING PROCESS APPROACH: APPLICATION TO DIABETICS Triastuti Wuryandari; Yuciana Wilandari
MEDIA STATISTIKA Vol 16, No 1 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.1.67-75

Abstract

Survival analysis is a branch of statistics for analyzing the duration of time until one or more events occur. Time to recurrence of diabetics including survival data. Diabetes can’t be cured but it can be controlled. Diabetics who don’t maintain their health and lifestyle will experience recurrence. Factors thought to influence the recurrence of diabetics are internal factors such as genetics and external factors such as lifestyle. The recurrence time of an object includes recurrent events because each object can experience the same recurrent event during the follow-up. One of the analysis to determine factors that are thought to influence the recurrence time of diabetics is survival analysis. Survival data can be modeled into a regression model if the survival time of an object is influenced by other factors. One of the regression models for survival data is Cox regression. One of the Cox regression models for recurrent event data is the AG model which uses a counting process approach. This study used data on the recurrence of diabetics at MH Thamrin Cileungsi Hospital. Based on data analysis, factors that influence the recurrence of diabetics are age, gender, and type of complication.
AN ADDITIVE SUBDISTRIBUTION HAZARDS MODEL FOR COMPETING RISKS DATA Molydah S, Molydah S; Danardono, Danardono
MEDIA STATISTIKA Vol 16, No 2 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.2.194-205

Abstract

Competing risk failure time data occur frequently in medical a number of methods have been proposed for the analysis of these data. The classic approach is to model all cause-specific hazards and then estimate the cumulative incidence curve based on these cause-specific hazards. Unfortunately, the cause-specific hazard function does not have a direct interpretation in terms of survival probabilities for the particular failure type.  In this paper, we consider a more flexible model for the subdistribution. It is a combination of the additive model and the Cox model and allows one to perform a more detailed study of covariate effects. One advantage of this approach is that our regression modeling allows for non-proportional hazards. This leads to a new simple goodness-of-fit procedure for the proportional subdistribution hazards assumption that is very easy to use. We applied this method to melanoma data and estimated the cumulative death rate for those who died from melanoma after surgical removal of the tumor. It was found that two covariates had a time-varying effect and two other covariates had a constant effect in predicting the cumulative incidence curve in patients who died of melanoma following tumor removal surgery.
PER CAPITA CONSUMPTION ESTIMATION IN SURABAYA USING ENSEMBLE MODEL APPROACH Sutikno, Sutikno; Purnomo, Jerry Dwi Trijoyo; Harfianto, Unggul; Irfandi, Yoga Prastya; Anisa, Kartika Nur; Cahyoko, Fajar Dwi
MEDIA STATISTIKA Vol 16, No 2 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.2.170-181

Abstract

The categorization of the Low-Income Community category is based on the poverty indicators in the Multidimensional Poverty Index, including the dimensions of health, education, and living standards. The Proxy Means Test (PMT) can estimate household income or consumption by taking into account household conditions that are readily observable and cannot be manipulated. This method offers the advantage of being capable of determining both the poverty level of a household and the household's characteristics based on asset ownership and socio-demographic conditions. This study aims to estimate per capita consumption using OLS, Robust, Quantile, LASSO, and Ensemble methods. The application of these methods is intended to address various issues, including the presence of outlier data, multicollinearity, and uncertainties. The results indicate that none of the four methods used achieved the highest accuracy based on the MSE, MAE, and sMAPE criteria. Consequently, employing an ensemble model becomes essential to accommodate the element of uncertainty present in these four models. The application of the ensemble method is not only as a comparison between the models, but also as a means to capture the uncertainty contained in each model
SIMULATION STUDY FOR UNDERSTANDING THE PERFORMANCE OF PARTIAL LEAST SQUARES–MODIFIED FUZZY CLUSTERING (PLSMFC) IN FINDING GROUPS UNDER STRUCTURAL EQUATION MODEL Moch. Abdul Mukid; Bambang Widjanarko Otok; Suparti Suparti
MEDIA STATISTIKA Vol 16, No 1 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.1.76-87

Abstract

In structural equation modeling (SEM), it is usually assumed that all observations follow only one model. This becomes irrelevant if the observations contain natural groups, each of which has a different SEM model. Mukid et al (2002) have proposed the partial least squares-modified fuzzy clustering method (PLSMFC) as a way to find groups of observations and at the same time estimate the parameters of the SEM model. This research aims to understand the performance of the PLSMFC method in finding groups of observations characterized by different forms of structural equation models. The goal was achieved by conducting a simulation study involving factors such as SEM model specification and number of clusters. The procedure used is to force the generated data into a different number of segments. The segment validity measures used are the fuzziness performance index (FPI) and normalized classification entropy (NCE). The correct number of segments is indicated by the smallest FPI and NCE values. Based on simulation studies, it is known that the PLSMFC method can detect segments accurately, especially if the size of the segments used to reallocate observations is larger than the number of segments used to generate the data.
HANDLING OF OVERDISPERSION CASES IN MORBIDITY DATA IN SELUMA REGENCY Sarumpaet, Mey Yanti; Nugroho, Sigit; Rachmawati, Ramya
MEDIA STATISTIKA Vol 16, No 2 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.2.206-214

Abstract

The problem of overdispersion as a violation of the assumption of equidispersion in Poisson regression is generally caused by  sources of unobserved heterogeneity, missing observations on predictor variables, outliers in the data, errors in the specification of the bridging function, and many observed  values that are zero.  The  purpose of  this study is  to find out the right  model and the variables  that affect data that occurs overdispersion and excess zero in the case of the number of days of disruption at work, school, or other daily activities due to health complaints. The methods used were Poisson Regression, Negative  Binomial Regression, Hurdle  Poisson  Regression,  Zero  Inflated Poisson Regression,  Zero  Inflated  Negative  Binomial Regression, and Hurdle Negative Binomial Regression. The data used were morbidity taken from data on the number of days  of  disruption at  work,  school  or  other daily  activities due  to  health  complaints  in  Seluma district,  Bengkulu Province. It was found that the best model is Zero Inflated Negative  Poisson  with  the  smallest  Akaike  Information Criterion (AIC) value of 1620.609  and the variables that have  a  significant  effect on the  log model and the logit model are marital status and work variables.
PERFORMANCE OF NEURAL NETWORK IN PREDICTING MENTAL HEALTH STATUS OF PATIENTS WITH PULMONARY TUBERCULOSIS: A LONGITUDINAL STUDY Rahmanda, Lalu Ramzy; Fernandes, Adji Achmad Rinaldo; Solimun, Solimun; Ramifidiosa, Lucius; Zamelina, Armando Jacquis Federal
MEDIA STATISTIKA Vol 16, No 2 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.2.124-135

Abstract

Comorbidity between pulmonary tuberculosis and mental health status requires effective psychiatric treatment. This study aims to predict anxiety and depression levels in patients with pulmonary tuberculosis and consider future mental health treatment for patients. A sample of 60 pulmonary tuberculosis patients in Malang were involved and evaluated longitudinally every two weeks over 13 periods. In this study, we use the Generalized Neural Network Mixed Model (GNMM) to obtain better results in predicting anxiety and depression levels in patients with pulmonary tuberculosis and compare the results with the Generalized Linear Mixed Model (GLMM). The flexibility of GLMM in modeling longitudinal data, and the power of neural network in performing a prediction makes GNMM a powerful tool for predicting longitudinal data. The result shows that neural network's prediction performance is better than the classical GLMM with a smaller MSPE and fairly accurate prediction. The MSPEs of the three compared models: 1-Layer GNMM, 2-Layer, and GLMM, respectively are 0.0067, 0.0075, 0.0321 for the anxiety levels, and 0.0071, 0.0002, and 0.0775 for the depression levels. Furthermore, future research needs to investigate the data with a larger sample size or high dimensional data with large network architectures to prove the robustness of GNMM.
BETA-BINOMIAL MODEL IN SMALL AREA ESTIMATION USING HIERARCHICAL LIKELIHOOD APPROACH Etis Sunandi; Khairil Anwar Notodiputro; Indahwati Indahwati; Agus Mohamad Soleh
MEDIA STATISTIKA Vol 16, No 1 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.1.88-99

Abstract

Small Area Estimation is a statistical method used to estimate parameters in sub-populations with small or even no sample sizes. This research aims to evaluate the Beta-Binomial model's performance for estimating small areas at the area level. The estimation method used is Hierarchical Likelihood (HL). The data used are simulation data and empirical data. Simulation studies were used to investigate the proposed model. The estimator's Mean Squared Error of Prediction (MSEP) and Absolute Bias (AB) estimator values determine the best estimation criteria. An empirical study using data on the illiteracy rate at the sub-district level in Bengkulu Province. The results of the simulation study show that, in general, the parameter estimators are nearly unbiased. Proportion prediction has the same tendency as parameters. Finally, the HL estimator has a small MSEP estimator. The results of an empirical study show that the average illiteracy rate in Bengkulu province is quite diverse. Kepahiang District has the highest average illiteracy rate in Bengkulu Province in 2021.
KAPLAN-MEIER AND NELSON-AALEN ESTIMATORS FOR CREDIT SCORING Tatik Widiharih; Agus Rusgiyono; Sudarno Sudarno; Bagus Arya Saputra
MEDIA STATISTIKA Vol 16, No 1 (2023): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.16.1.37-46

Abstract

Financial institutions use credit scoring analysis to predict the probability that a customer will default. In this paper, we determine the probability of default using nonparametric survival analysis that are Kaplan-Meier and Nelson-Aalen. The analysis is based on survival function curves, cumulative hazard function curves, mean survival time, and standard error of estimators. Based on the curves of survival function for both Kaplan Meier and Nelson Aalen estimators relatively the same. Based on the curves of cumulative hazard function, mean survival time, and standard error the Nelson-Aalen estimators are slightly higher than Kaplan-Meier.

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