EWMA

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial least squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal. Copyright © 2006 John Wiley & Sons, Ltd.

Purpose
The current paper aims to fill the gap in the literature by analyzing the nature of volatility of the Karachi Stock Exchange (KSE) -100 index and also develops an understanding as to which model proves to be the most suitable model for measuring volatility among the models used. The study contributes significantly to the literature as compared to the limited previous work done in Pakistan as it covers a longer time period from the introduction of the KSE-100 index on November 2, 1991 to December 31, 2013 for three types of data i.e., daily, weekly and monthly. In addition, to analyze the impact of global financial crises upon volatility, the data is divided into pre-crisis (1991-2007) and post-crisis (2008-2013) periods.
Design/Methodology/Approach
This study employs an advanced set of volatility models such as Autoregressive Conditional Heteroscedasticity [ARCH (1)]; Generalized Autoregressive Conditional Heteroscedasticity [GARCH (1, 1)]; GARCH in Mean [GARCH-M (1, 1)]; Exponential GARCH [E-GARCH (1, 1)]; Threshold GARCH [T-GARCH (1, 1)]; Power GARCH [P-GARCH (1, 1)] and also a simple Exponentially Weighted Moving Average (EWMA) model.
Findings
The results reveal that daily, weekly and monthly return series show non-normal distribution, stationarity, and volatility clustering. However, the heteroscedasticity is absent only in the monthly returns making only EWMA model to be used in monthly series to measure the volatility level. P-GARCH (1, 1) model proves to be a better model for modeling the volatility in case of daily returns; while regarding the weekly data GARCH (1, 1) model proves to be the most appropriate based on SIC and LL criterion. The study shows a high persistence of volatility, a mean reverting process, and absence of risk premium in the KSE market with an insignificant leverage effect only in case of weekly returns; however a significant leverage effect is reported regarding the daily series of the KSE-100 index. In addition, to analyze the impact of global financial crises upon volatility, the findings show that sub-periods demonstrated a slightly low volatility and global economic crisis did not cause a rise in volatility level.
Originality/Value
The literature about the volatility modeling in the Pakistan market depicts a limited literature focus on few models for relatively small sample size. The current thesis attempts to overcome these limitations and employs diverse models for three types of data series (daily, weekly and monthly). In addition, Pakistani economy shows turmoil throughout its history, which ranges from mild shocks to extreme shocks. This paper measures the impact of those shocks upon the volatility level of the KSE.
Applications
The research provides some insights for policy makers as well as investors who are concerned about the fluctuations of the KSE-100 index in Pakistan. For example, the significant ARCH effect may imply that the institutional investors who do not trade very frequently also hold dominance with respect to the movement of the price of stocks. As when they trade, it is in the large bulk and this heavy trade holds a significant impact on the price movements of the stock (Husain and Uppal, 1999). The skewness with the negative value in all return series imply that there are more chances of earning negative returns than the positive returns which refers towards the conservative attitude of investors towards investment in the KSE market (Mittal and Goyal, 2012; Kaluo and Friday, 2012).. The findings also reveal a high persistence of volatility and it contributes to the existence of the impact of shock, observed in present, for a longer time on future returns. The high volatility presence makes it possible to earn high profit but it also leads towards an inefficient market (Mittal and Goyal, 2012). A presence of an insignificant risk premium implies that investors are unable to earn returns above the average by taking the higher risk. The leverage parameter finds significance in case of daily returns which implies that negative shocks account for the greater volatility in the KSE market; however, the weekly data do not reveal significant asymmetrical or leverage effect.

Key Words: ARCH, GARCH, EWMA, Global Financial Crisis, Pakistan, Volatility

Short-run productions are common in manufacturing environments like job shops, which are characterized by a high degree of flexibility and production variety. Owing to the limited number of possible inspections during a short run, often the Phase I control chart cannot be performed and correct estimates for the population mean and standard deviation are not available. Thus, the hypothesis of known in-control population parameters cannot be assumed and the usual control chart statistics to monitor the sample mean are not applicable. t-charts have been recently proposed in the literature to protect against errors in population standard deviation estimation due to the limitation of available sampling measures. In this paper the t-charts are tested for implementation in short production runs to monitor the process mean and their statistical properties are evaluated. Statistical performance measures properly designed to test the chart sensitivity during short runs have been considered to compare the performance of Shewhart and EWMA t-charts. Two initial setup conditions for the short run fixing the population mean exactly equal to the process target or, alternatively, introducing an initial setup error influencing the statistic distribution have been modelled. The numerical study considers several out-of-control process operating conditions including one-step shifts for the population mean and/or standard deviation. The obtained results show that the t-charts can be successfully implemented to monitor a short run. Finally, an illustrative example is presented to show the use of the investigated t charts. Copyright © 2010 John Wiley & Sons, Ltd.

Control charts are effective tool with regard to improving process quality and productivity, Shewhart control charts are efficiently good at detecting large shifts in a given process but very slow in detecting small and moderate shifts, such problem could be tackled through design of sensitizing rules. It has been observed that autocorrelation has an advert effect on the control charts developed under the independence assumption [1]. In this article a new EWMA control chart has been introduced with autocorrelation and some run rule schemes were introduced to enhanced the performance of the EWMA control chart when autocorrelated. The three-out-of three EWMA scheme and three-out-of- four EWMA schemes were introduced and the generated data with induced autocorrelation were used to construct the EWMA chart to sensitize the shifts presence.  Simulation of autocorrelated data were carried out for the proposed schemes which detects the shifts as soon as it occurs in the given process, the ...

The paper focuses on the algorithms of the event detection in content-based video retrieval. Video has a complex structure and can express the same idea in different ways. This makes the task of searching for video more complicated. Video titles and text descriptions cannot give the whole information about objects and events in the video. This creates a need for content-based video retrieval. There is a semantic gap between low-level video features, that can be extracted, and the users’ perception. The task of event detection is reduced to the task of video segmentation. Complex content-based video retrieval can be regarded as the bridge between traditional retrieval and semantic-based video retrieval. The properties of video as a time series are described. Introduced the concept of anomalies in the video. A method for event detection based on comparing moving averages with windows of different sizes is proposed. According to the classification given at the beginning of this article, our method refers to statistical methods. It differs from other methods with low computational complexity and simplicity. The video stream processing language is proposed for function-based description of video handling algorithms. So, our method is formulated in the form of a declarative description on an interpreted programming language. Unfortunately, most of the existing video processing methods use exclusively imperative approach, which often makes understanding more difficult. Examples of the use of this language are given. Its grammar is described too. As shown by experiments, the implementation of the proposed video events retrieval method, unlike their counterparts, can work for video streams too with a real-time and potentially infinite frame sequences. Such advantages within low computational requirements make implementation of the method helpful in aviation and space technology. The algorithm has some disadvantages due to necessity parameter selection for particular task classes. The theorem on near-duplicates of video is formulated at the end of the article. It asserts the near-duplicate videos express the same sequence of phenomena.

Short-run productions are common in manufacturing environments like job shops, which are characterized by a high degree of flexibility and production variety. Owing to the limited number of possible inspections during a short run, often the Phase I control chart cannot be performed and correct estimates for the population mean and standard deviation are not available. Thus, the hypothesis of known in-control population parameters cannot be assumed and the usual control chart statistics to monitor the sample mean are not applicable. t-charts have been recently proposed in the literature to protect against errors in population standard deviation estimation due to the limitation of available sampling measures. In this paper the t-charts are tested for implementation in short production runs to monitor the process mean and their statistical properties are evaluated. Statistical performance measures properly designed to test the chart sensitivity during short runs have been considered to compare the performance of Shewhart and EWMA t-charts. Two initial setup conditions for the short run fixing the population mean exactly equal to the process target or, alternatively, introducing an initial setup error influencing the statistic distribution have been modelled. The numerical study considers several out-of-control process operating conditions including one-step shifts for the population mean and/or standard deviation. The obtained results show that the t-charts can be successfully implemented to monitor a short run. Finally, an illustrative example is presented to show the use of the investigated t charts. Copyright © 2010 John Wiley & Sons, Ltd.

ABSTRACT Spatio-temporal surveillance methods for detecting outbreaks of disease are fairly common in the literature with the scan statistic setting the benchmark. If the shape and size of the outbreaks are known in advance, then the scan approach can be designed to efficiently detect these, however, this is seldom true. Therefore we want to devise plans that are efficient at detecting a number of outbreaks that vary in size and shape. This paper examines plans which use the exponential weighted moving average statistic to build temporal memory into plans and tries to develop robust plans for detecting outbreaks of unknown shapes and sizes.