• The Korean Journal of Applied Statistics
• /
• v.1 no.2
• /
• pp.10-26
• /
• 1987

If the forecasts from different, sources are combined in some way, the resulting forecasts may be more accurate than any of the individual components. In this paper, the established procedures of combining forecasts are reviewed and the alternative procedures are suggested. By the results of empirical analysis from survey data, the method of combining forecasts using the restricted regression weights, the restricted robust regression weights, and mixed regression weights are robust. We can not find the most efficient combined forecasts in any case if we select the corresponding decision by preliminary analysis for the statistical properties of individual dorecasts, our results of combined forecast can became useful.

• Journal of Biomedical Engineering Research
• /
• v.31 no.2
• /
• pp.134-140
• /
• 2010

Readily available high resolution brain MRI scans allow detailed visualization of the brain structures. Researchers have focused on developing methods to quantify shape differences specific to diseased scans. We have developed a novel method to quantify shape information for a specific population based on Multidimensional scaling(MDS). MDS is a well known tool in statistics and here we apply this classical tool to quantify shape change. Distance measures are required in MDS which are computed from pair-wise image registrations of the training set. Registration step establishes spatial correspondence among scans so that they can be compared in the same spatial framework. One benefit of our method is that it is quite robust to errors in registrations. Applying our method to 13 brain MRI showed clear separation between normal and diseased (Cushing's syndrome). Intentionally perturbing the image registration results did not significantly affect the separability of two clusters. We have developed a novel method to quantify shape based on MDS, which is robust to image mis-registration.

• Wind and Structures
• /
• v.26 no.6
• /
• pp.383-395
• /
• 2018

An accurate determination of wind speed distribution is the basis for an evaluation of the wind energy potential required to design a wind turbine, so it is important to estimate unknown parameters of wind speed distribution. In this paper, Gumbel distribution is used in modelling wind speed data, and alternative robust estimation methods to estimate its parameters are considered. The methodologies used to obtain the estimators of the parameters are least absolute deviation, weighted least absolute deviation, median/MAD and least median of squares. The performances of the estimators are compared with traditional estimation methods (i.e., maximum likelihood and least squares) according to bias, mean square deviation and total mean square deviation criteria using a Monte-Carlo simulation study for the data with and without outliers. The simulation results show that least median of squares and median/MAD estimators are more efficient than others for data with outliers in many cases. However, median/MAD estimator is not consistent for location parameter of Gumbel distribution in all cases. In real data application, it is firstly demonstrated that Gumbel distribution fits the daily mean wind speed data well and is also better one to model the data than Weibull distribution with respect to the root mean square error and coefficient of determination criteria. Next, the wind data modified by outliers is analysed to show the performance of the proposed estimators by using numerical and graphical methods.

• Proceedings of the IEEK Conference
• /
• 2003.07e
• /
• pp.2411-2414
• /
• 2003

In this paper, we present a digital image authentication using semi-fragile watermarking techniques. The algorithm is robust to innocuous manipulations while detecting malicious manipulations. Specifically, the proposed method is designed for the purpose of the real time authentication of an image frame captured from a digital camera due to its easy H/W implementation, security and visible verification. To achieve the semi-fragile characteristics that survive a certain amount of compression, we employ the invariant property of DCT coefficients' quantization proposed by Lin and Chang [1]. The binary watermark bits are generated by exclusive ORing the binary logo with pseudo random binary sequences. Then watermark bits are embedded into the LSBs of pre-quantized DCT coefficients in the medium frequency range. Verification is carried out easily due to visually recognizable pattern of the logo extracted by exclusive ORing the LSBs of the embedded DCT coefficient with pseudo random number seeded by a secret key. By the experiment results, this method is not only robust to JPEG compression but also it detects powerfully alterations of the original image, such as the tempering of images.

• Journal of Internet Computing and Services
• /
• v.1 no.1
• /
• pp.63-72
• /
• 2000

Data mining means a process of nontrivial extraction of hidden knowledge or potentially useful information from data in large databases. Data mining algorithm is a multi-disciplinary field of research; machine learning, statistics, and computer science all make a contribution. Different classification schemes can be used to categorize data mining methods based on the kinds of tasks to be implemented and the kinds of application classes to be utilized, and classification has been identified as an important task in the emerging field of data mining. Since classification is the basic element of human's way of thinking, it is a well-studied problem in a wide varietyof application. In this paper, we propose a classifier system based on genetic algorithm with robust property, and the proposed system is evaluated by applying it to nDmC problem related to classification task in data mining.

• The Mathematical Education
• /
• v.45 no.2 s.113
• /
• pp.145-154
• /
• 2006

The main purpose of this study is to investigate the effect of departures from normality and equal variance on the two-sample test when the variances are unknown. We have found that type I error brought about a little bit change which is ignorable in relation to kurtosis. But the change of type I error was mainly based on the skewness of the parent population. In introductory statistics classes where data analysis includes techniques for detecting skewness of two populations, we recommend the two-sample t-test when maximal skewness of two populations is smalter than the value 4 when the variances seem equal. Furthermore, our simulations reveal that the two-sample t-test appears somewhat more robust than that of z-test if the assumption of equal variance is satisfied. In the case of unequal variance, the two-sample t-test appears somewhat more robust provided the t-statistic using Satterthwaite's approximate degrees of freedom.

• International Journal of Reliability and Applications
• /
• v.7 no.2
• /
• pp.101-110
• /
• 2006

Let E be a set of n quantitative observations under the time control. The interval of time is to be split into several subintervals such that the observations in each subinterval are almost similar, whereas the observations between the subintervals are very dissimilar. The corresponding time-subintervals become periods or phases of the development that exist in the underlying phenomenon. Aboukalam(2005) proposes a robust solution based on some initial subintervals and a technique for combining any two successive groups in that starter using a t-test under a fixed significant level ($\alpha$). The inconvenience is that; the technique reliability is not released from the level $\alpha$ which must not be defined apart from the number of the periods that is, in its turn, unknown. To avoid this, we propose what so called; most significant periods solution. The new technique constructs its own initial subintervals and uses another way for combining the groups. However, the way of determining and treating outliers has not changed. This paper conducts many empirical simulations using different possible time dominated data in order to illustrate the reliability of the proposed technique. Finally, we apply both techniques on some real time dominated data to explain the advantage of the proposal.

• Proceedings of the KIEE Conference
• /
• 2007.07a
• /
• pp.1797-1798
• /
• 2007

In field of voice pathology, diverse statistics extracted form pitch estimation were commonly used to assess voice quality. In this study, we proposed robust pitch detection algorithm which can estimate pitch of pathological voices in benign vocal fold lesions. we also compared our proposed algorithm with three established pitch detection algorithms; autocorrelation, simplified inverse filtering technique, and nonlinear state-space embedding methods. In the database of total pathological voices of 99 and normal voices of 30, an analysis of errors related with pitch detection was evaluated between pathological and normal voices, or among the types of pathological voices. According to the results of pitch errors, gross pitch error showed some increases in cases of pathological voices; especially excessive increase in PDA based on nonlinear time-series. In an analysis of types of pathological voices classified by aperiodicity and the degree of chaos, the more voice has aperiodic and chaotic, the more growth of pitch errors increased. Consequently, it is required to survey the severity of tested voice in order to obtain accurate pitch estimates.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.31 no.6
• /
• pp.1171-1179
• /
• 2021

Image forensic is performed to check image limpidness. In this paper, a robust scheme is discussed to detect median filtering in low quality images. Detection of median filtering assists in overall image forensic. Improved spatial statistical features are extracted from the image to classify pristine and median filtered images. Image array data is rescaled to enhance the spatial statistical information. Features are extracted using Markov model on enhanced spatial statistics. Multiple difference arrays are considered in different directions for robust feature set. Further, texture operator features are combined to increase the detection accuracy and SVM binary classifier is applied to train the classification model. Experimental results are promising for images of low quality JPEG compression.

• The Journal of Asian Finance, Economics and Business
• /
• v.9 no.7
• /
• pp.1-15
• /
• 2022

Combining the strength of both Mixed Data Sampling (MIDAS) Regression and realized variance measures, this paper seeks to investigate two objectives: (1) evaluate the post-sample performance of the proposed weekly Realized Variance-MIDAS (RVar-MIDAS) in one-week ahead volatility forecasting against the established Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and the less explored but robust STES (Smooth Transition Exponential Smoothing) methods. (2) comparing forecast error performance between realized variance and squared residuals measures as a proxy for actual volatility. Data of seven private equity mutual fund indices (generated from 57 individual funds) from two different time periods (with and without financial crisis) are applied to 21 models. Robustness of the post-sample volatility forecasting of all models is validated by the Model Confidence Set (MCS) Procedures and revealed: (1) The weekly RVar-MIDAS model emerged as the best model, outperformed the robust DAILY-STES methods, and the weekly DAILY-GARCH models, particularly during a volatile period. (2) models with realized variance measured in estimation and as a proxy for actual volatility outperformed those using squared residual. This study contributes an empirical approach to one-week ahead volatility forecasting of mutual funds return, which is less explored in past literature on financial volatility forecasting compared to stocks volatility.