Mean-variance portfolios constructed using the sample mean and covariance matrix of asset returns perform poorly out-of-sample due to estimation error. Recently, there are two approaches designed to reduce the effect of estimation error: robust statistics and robust optimization. Two different robust portfolios were examined by assessing the outof-sample performance and the stability of optimal portfolio compositions. The performance of the proposed robust portfolios was compared to classical portfolios via expected return, risk, and Sharpe Ratio. The aim is to shed light on the debate concerning the importance of the estimation error and weights stability in the portfolio allocation problem, and the potential benefits coming from robust strategies in comparison to classical portfolios.

Information Technology (IT) represent one of main indicator to support the academic atmosphere at the university. Therefore UIN Sunan Kalijaga (Suka) Yogyakarta has owned sistem information technology and it is called Academic Information System (SIA). UIN Suka shall has knowledge preference and perception of consumer to the service, which is like what required by consumer. By using Conjoint Analysis method would have been obtained combination from level-level factor (stimuly) took a fancy by consumer according to value of highest utility from every level factor.  The objective of this research is to measure preference level of consumers (students) to the SIA services in UIN Suka used Conjoint Analysis method. The result shows that the most important factor in using SIA service is the benefit (importance value is 66,623%, the second important factor is accesibility of SIA ( importance value is 19,227%) and the last important is ability of staff  (importance value is 14,15%). According to value of utility estimate, it shows that consumers like to use SIA for key in courses (utility estimate is 2,104), online service (utility estimate is 0,577) and SIA staff who are very friendly when they were servicing the students (utility estimate is 0,210).

No abstract

Piecewise constant (PC) is a stochastic model that can be applied in various fields such as engineering and ecology. The stochastic model contains a noise. The accuracy of the stochastic model in modeling a signal is influenced by the type of noise. This paper aims to propose inverse-gamma noise in the PC model and the procedure for estimating the model parameters. The model parameters are estimated using the Bayes approach. Model parameters have a variable dimension space so that the Bayesian estimator cannot be determined analytically. Therefore, the Bayesian estimator is calculated using the reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm. The performance of the RJMCMC algorithm is validated using data synthesis. The finding is a new PC model in which the noise has an inverse-gamma distribution. In addition, this paper also proposes a parameter estimation procedure for the model based on an RJMCMC. The simulation study shows that the model parameter estimators generated by this algorithm are close to the model parameter values. This paper concludes that inverse gamma noise can be used as an alternative noise in the PC model. The RJMCMC is categorized as a valid algorithm and can estimate the PC model parameters where the noise has an inverse-gamma distribution. The novelty in this paper is the development of a new stochastic model and the procedure for estimating the model parameters. In application, the findings in this paper have the potential to improve the suitability of the stochastic model to the signal.

The Human Development Index (HDI) is a regional or national welfare index based on three aspects, namely a long and healthy life, knowledge, and a decent standard of living. This study aims to determine the factors that affect the HDI of Regency/City in the Province of the Special Region of Yogyakarta (DIY) in 2016-2021. These factors include poverty rates, average length of schooling, gross regional domestic product, and health complaints. The analysis used is panel data regression. The influencing factors are the variable of poverty level, average length of schooling and gross regional domestic product.