• Communications for Statistical Applications and Methods
• /
• v.22 no.3
• /
• pp.295-304
• /
• 2015

This paper considers the problem of estimation of the Hurst parameter H ${\in}$ (1/2, 1) from longitudinal data with the error term of a fractional Brownian motion with Hurst parameter H that gives the amount of the long memory of its increment. We provide a new estimator of Hurst parameter H using a two scale sampling method based on $A{\ddot{i}}t$-Sahalia and Jacod (2009). Asymptotic behaviors (consistent and central limit theorem) of the proposed estimator will be investigated. For the proof of a central limit theorem, we use recent results on necessary and sufficient conditions for multi-dimensional vectors of multiple stochastic integrals to converges in distribution to multivariate normal distribution studied by Nourdin et al. (2010), Nualart and Ortiz-Latorre (2008), and Peccati and Tudor (2005).

• Communications for Statistical Applications and Methods
• /
• v.20 no.6
• /
• pp.439-454
• /
• 2013

This study presents a Bayesian multiple change-point detection approach to segment and classify the observations that no longer come from an initial population after a certain time. Inferences are based on the multiple change-points in a sequence of random variables where the probability distribution changes. Bayesian multiple change-point estimation is classifies each observation into a segment. We use a truncated Poisson distribution for the number of change-points and conjugate prior for the exponential family distributions. The Bayesian method can lead the unsupervised classification of discrete, continuous variables and multivariate vectors based on latent class models; therefore, the solution for change-points corresponds to the stochastic partitions of observed data. We demonstrate segmentation with real data.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.29 no.1 s.232
• /
• pp.59-66
• /
• 2005

In this paper, a robust stationary Kalman filter is designed by minimizing selected performance indices so that it is less sensitive to uncertainties. The uncertainties include not only stochastic factors such as process noise and measurement noise, but also deterministic factors such as unknown initial estimation error, modeling error and sensing bias. To reduce the effect on the uncertainties, three performance indices that should be minimized are selected based on the quantitative error analysis to both the deterministic and the stochastic uncertainties. The selected indices are the size of the observer gain, the condition number of the observer matrix, and the estimation error variance. The observer gain is obtained by optimally solving the multi-objectives optimization problem that minimizes the indices. The robustness of the proposed filter is demonstrated through the comparison with the standard Kalman filter.

• Communications for Statistical Applications and Methods
• /
• v.30 no.2
• /
• pp.191-213
• /
• 2023

Unit distributions are frequently used in probability theory and statistics to depict meaningful variables having values between zero and one. Using convenient transformation, the unit inverse exponentiated weibull (UIEW) distribution, which is equally useful for modelling data on the unit interval, is proposed in this study. Quantile function, moments, incomplete moments, uncertainty measures, stochastic ordering, and stress-strength reliability are among the statistical properties provided for this distribution. To estimate the parameters associated to the recommended distribution, well-known estimation techniques including maximum likelihood, maximum product of spacings, least squares, weighted least squares, Cramer von Mises, Anderson-Darling, and Bayesian are utilised. Using simulated data, we compare how well the various estimators perform. According to the simulated outputs, the maximum product of spacing estimates has lower values of accuracy measures than alternative estimates in majority of situations. For two real datasets, the proposed model outperforms the beta, Kumaraswamy, unit Gompartz, unit Lomax and complementary unit weibull distributions based on various comparative indicators.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
• /
• 2007.11a
• /
• pp.269-275
• /
• 2007

• Journal of the Korean Data and Information Science Society
• /
• v.28 no.6
• /
• pp.1447-1456
• /
• 2017

Accurate inference of parameters of a stochastic rainfall generation model is essential to improve the applicability of the rainfall generation model which modeled the rainfall process and the structure of rainfall events. In this study, the model parameters of a stochastic rainfall generation model, NSRPM (Neyman-Scott rectangular pulse model), were estimated using DFP (Davidon-Fletcher-Powell), GA (genetic algorithm), Nelder-Mead, and DE (differential evolution) methods. Summer season hourly rainfall data of 20 rainfall observation sites within the Nakdong river basin from 1973 to 2017 were used to estimate parameters and the regional applicability of inference methods were analyzed. Overall results demonstrated that DE and Nelder-Mead methods generate better results than that of DFP and GA methods.

• Journal of the Korea Society of Computer and Information
• /
• v.25 no.6
• /
• pp.119-129
• /
• 2020

Software development activities are influenced by stochastic theory rather than deterministic one due to having process variability. Stochastic methods factor in the uncertainties associated with project activities and provides insight into the expected project outputs as probability distributions rather than as deterministic approximations. Thus, successful software projects systematically manage and balance five objectives based on historical probability: scope, size, cost, effort, schedule, and quality. Although software size estimation having much uncertainty in initial development has traditionally performed using deterministic methods: LOC(Lines Of Code), COCOMO(COnsructive COst MOdel), FP(Function Point), SLIM(Software LIfecycle Management). This research aims to present a function point method based on stochastic distribution and a case study based on Monte Carlo Simulation applying on an automotive electrical and electronics system software development. It is expected that the result of this paper is used as guidance for establishing of function point method in organizations and tools for helping project managers make decisions correctly.

• Journal of Korean Society of Transportation
• /
• v.24 no.4 s.90
• /
• pp.149-159
• /
• 2006

Traditional trip tables are estimated through large-scale surveys such as household survey, roadside interviews, and license Plate matching. These methods are, however, expensive and time consuming. This paper presents two origin-destination (OD) trip matrix estimation methods from link traffic counts in stochastic assignment, which contains perceived errors of drivers for alternatives. The methods are formulated based on the relation between link flows and OD demands in logit formula. The first method can be expressed to minimize the difference between observed link flows and estimated flows, derived from traffic assignment and be solved by gradient method. The second method can be formulated based on dynamic process, which nay describe the daily movement patterns of drivers and be solved by a recursive equation. A numerical example is used for assessing the methods, and shows the performances and properties of the models.

• Smart Structures and Systems
• /
• v.31 no.2
• /
• pp.113-130
• /
• 2023

Hybrid simulation (HS) has attracted community attention in recent years as an efficient and effective experimental technique for structural performance evaluation in size-limited laboratories. Traditional hybrid simulations usually take deterministic properties for their numerical substructures therefore could not account for inherent uncertainties within the engineering structures to provide probabilistic performance assessment. Reliable structural performance evaluation, therefore, calls for stochastic hybrid simulation (SHS) to explicitly account for substructure uncertainties. The experimental design of SHS is explored in this study to account for uncertainties within analytical substructures. Both computational simulation and laboratory experiments are conducted to evaluate the pseudo-random Sobol sequence for the experimental design of SHS. Meta-modeling through polynomial chaos expansion (PCE) is established from a computational simulation of a nonlinear single-degree-of-freedom (SDOF) structure to evaluate the influence of nonlinear behavior and ground motions uncertainties. A series of hybrid simulations are further conducted in the laboratory to validate the findings from computational analysis. It is shown that the Sobol sequence provides a good starting point for the experimental design of stochastic hybrid simulation. However, nonlinear structural behavior involving stiffness and strength degradation could significantly increase the number of hybrid simulations to acquire accurate statistical estimation for the structural response of interests. Compared with the statistical moments calculated directly from hybrid simulations in the laboratory, the meta-model through PCE gives more accurate estimation, therefore, providing a more effective way for uncertainty quantification.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.2D
• /
• pp.251-258
• /
• 2006

This study is to develop an estimation method of freeway design capacity through the analysis of time headway distribution in continuum flow. Traffic flow-speed diagram and time headway distribution plotted from individual vehicle data shows: a) a road capacity is not deterministic but stochastic, b) time headway distribution for each vehicle speed group follows pearson type V distribution. The freeway design capacity estimation model is developed by determining a minimum time headway for capacity with stochastic method. The estimated capacity values for each design speed are lower when design speed ${\leq}80km/h$, and higher when design speed ${\geq}106km/h$ in comparison with HCM(2000)'s values. In addition, The distinguish difference is that this model leads flexible application in planning level by defining the capacity as stochastic distribution. In detail, this model could prevent a disutility to add a lane for only one excess demand in a road planning level.