scholarly journals Estimation of AR (2) Model with Dependent Errors for Unbounded Stationary and Nonstationary Time Series

2021 ◽  
Vol 23 (12) ◽  
pp. 417-422
Author(s):  
Prof. Ahmed Amin EL- Sheikh ◽  
◽  
Mohammed Ahmed Farouk Ahmed ◽  
Keyword(s):  
Time Series ◽  
Sample Size ◽  
Covariance Matrix ◽  
Nonstationary Time Series ◽  
Stationary Time Series ◽  
Times Series ◽  
The Third ◽  
Stationary Conditions ◽  
Simulation Results ◽  
Better Than

In this paper the GLS and the ML estimators, the variance-covariance matrix, the unbiased for the GLS and the ML estimators of parameters of AR (2) model with constant in case of dependent errors have been derived, the simulation results shown that the values of MSE and Thiel’s U in case of unbounded stationary time series for all sample size T are less than the values of MSE and Thiel’s U in case of unbounded nonstationary time series which approved that the results for unbounded stationary times series are better than the results for unbounded nonstationary times series, and the simulation results for unbounded nonstationary time series shown that by using the measurement of MSE the best case among of all cases of nonstationary which gives the smallest values of MSE is case four when the first and the second conditions of stationary conditions for AR (2) model are exists, while by using the measurement of Thiel’s U the best case among of all cases of nonstationary which gives the smallest values of Thiel’s U is case six when the second and the third conditions of stationary conditions for AR (2) model are exists.

scholarly journals Istikhdam Uslub Dji Sam Soe (234) fi Qira'ah An-Nushush Ghair Masykulah

2019 ◽  
Vol 21 (02) ◽  
pp. 155
Author(s):  
Faisal Mubarak Seff ◽  
Ridha Darmawaty
Keyword(s):  
Time Series ◽  
Sample Selection ◽  
Selection Technique ◽  
Average Ability ◽  
The Third ◽  
Series Design ◽  
Group Time ◽  
Quasi Experimental ◽  
Academic Year ◽  
Better Than

The superior ability to read Arabic texts without charity is a must for PBA students. But in reality they have difficulty in this because most of the material in the language language they study is rarely used in producing language, especially with the use of various terms that exacerbate these difficulties. Therefore, a strategy that is able to assist them in applying language rules in reading scripts with the use of simple terms and explanations is a must. And the strategy of Dji Sam Soe (234) is one solution with the character of the problem at hand. This study aims to describe the effectiveness of the strategy of Dji Sam Soe (234) in reading texts without a mark on cross-level PBA students in the 2016-2017 academic year by using the Quasi experimental design of one group time series design. Porpusif sampling is a sample selection technique, which is students in the third, fifth and seventh semester who have participated in Qiraatul Kutub supporting courses with instruments in the form of sentences and paragraphs. The results of this study indicate that there is an increase in the ability to read manuscripts without harakat in the form of sentences or paragraphs at each level, although the average ability is slightly different in the ability to read texts in paragraph forms, where seventh semester students are better than semesters three and five.

Algorithms Research in the Application of Lorenz Time Series Prediction

2011 ◽  
Vol 268-270 ◽  
pp. 1017-1020
Author(s):  
Man Xiang Miao ◽  
Yi Jin Gang
Keyword(s):  
Time Series ◽  
Statistical Learning ◽  
Time Series Prediction ◽  
Chaotic Time Series ◽  
Support Vector ◽  
Rbf Networks ◽  
Machine Learning Methods ◽  
Simulation Results ◽  
Time Series Simulation ◽  
Better Than

Prediction of Lorenz Chaotic Time Series is a vital problem in nonlinear dynamics .Support vector machine (SVM) is a kind of novel machine learning methods based on statistical learning theory, which have been provided an efficient algorithm thought in prediction of Chaotic Time Series. This paper combined SVM with neural network which based on the similarity of structure between SVM and RBF Networks, using SVM to obtain the centers of RBF Networks, then to predict the Lorenz Chaotic Time Series. Simulation results show that the effect is better than other methods.

Orthogonal Equivariant Minimax Estimatorsof Bivariate Normal Covariance Matrix and Precision Matrix

1983 ◽  
Vol 32 (1-2) ◽  
pp. 23-46 ◽  
Author(s):  
Divakar Sharma ◽  
K. Krishnamoorthy
Keyword(s):  
Covariance Matrix ◽  
Loss Function ◽  
Unbiased Estimator ◽  
Wishart Distribution ◽  
Loss Functions ◽  
Precision Matrix ◽  
Bivariate Normal ◽  
Minimax Estimators ◽  
Simulation Results ◽  
Better Than

The scale and orthogonal equivariant minimax estimators are obtained for the bivariate normal covariance matrix and precision matrix under Selliah's (1964} and Stein's (1961) loss functions. These new estimators are better than Selliah's and Stein's minimax estimators. An unbiased estimator of the risk of the new estimator is obtained under Selliah's loss function using Haff's (1979) identity for the Wishart distribution. Simulation results seem to indicate that the new estimators dominate the corresponding Hatf's [(1979), (1980)] estimators. We also prove that, for p-2, Haff's estimators are not minimax.

scholarly journals Over-Differencing and Forecasting with Non-Stationary Time Series Data

2019 ◽  
Vol 67 (1) ◽  
pp. 21-26
Author(s):  
Zakir Hossain ◽  
Atikur Rahman ◽  
Moyazzem Hossain ◽  
Jamil Hasan Karami
Keyword(s):  
Time Series ◽  
Simulation Study ◽  
Time Series Data ◽  
House Prices ◽  
Stationary Time Series ◽  
Series Data ◽  
Common Phenomenon ◽  
Stationary Time ◽  
True Model ◽  
Simulation Results

In time series analysis, over-differencing is a common phenomenon to make the data to be stationary. However, it is not always a good idea to take over-differencing in order to ensure the stationarity of time series data. In this paper, the effect of over-differencing has been investigated via a simulation study to observe how far or how close the fitted model from the true one. Simulation results show that the fitted model is found to be different and very far from the true model because of over-differencing in most of the scenarios considered in this study. In practice, it may be worthy to consider differencing as well as suitable transformation of the time series data to make it stationary. Both transformation and differencing are used for a non-stationary time series data on average monthly house prices to ensure it to be stationary. We then analyse the data and make prediction for the future values. Dhaka Univ. J. Sci. 67(1): 21-26, 2019 (January)

scholarly journals COVRATIO Statistic as a Discrimination Method for Multivariate Normal Distribution

2021 ◽  
Vol 50 (7) ◽  
pp. 2079-2084
Author(s):  
Norli Anida Abdullah ◽  
Afera Mohamad Apandi ◽  
Mohd Iqbal Shamsudheen ◽  
Yong Zulina Zubairi
Keyword(s):  
Normal Distribution ◽  
Covariance Matrix ◽  
Multivariate Normal Distribution ◽  
Discrimination Function ◽  
Multivariate Normal ◽  
Full Dataset ◽  
Linear Discrimination ◽  
Simulation Results ◽  
Better Than ◽  
Discrimination Method

The COVRATIO statistic has been used to identify the presence of outlier in data, which is based on deletion approach, where the determinant of covariance matrix for the full dataset excludes i-th row. This study proposes a novel discrimination method for the multivariate normal (MVN) distribution using the idea of COVRATIO statistic, denoted as . The linear discrimination function (LDF) for MVN distribution will be compared to the statistic. Simulation results showed that the as discrimination method performs better than the LDF with lower misclassification probabilities in all cases considered. The interest in the discrimination method arose in connection with the study of an application to discriminate the shape of the human maxillary dental arches, thus statistic may be considered as an alternative.

Point-Wise Dimension Estimate of Nonstationary, Multi-Episode, Time-Series and Application to Gearbox Signal

1999 ◽  
Author(s):  
D. C. Lin ◽  
B. J. Augustine ◽  
M. F. Golnaraghi
Keyword(s):  
Time Series ◽  
First Passage Time ◽  
Passage Time ◽  
Nonstationary Time Series ◽  
Stationary Time Series ◽  
Dimension Estimate ◽  
Estimation Algorithms ◽  
Data Segment ◽  
Multiple Episode ◽  
Autocorrelation Time

Abstract Dimensions of nonstationary time series is studied. The nonstationarity is considered to be due to multiple episode where an episode is a piece of stationary time series. The dimension estimation algorithms in the literature can be naturally extended to study multi-episode time series by restricting the calculation on data segment of pre-determined length. Inevitably, more than one episode will be included in the segment. This work focuses on finding when such dimension estimate has a local interpretation as the dimension of the episode. It was found that the local interpretation is valid if there is a large enough difference in the autocorrelation time of the episodes. This is termed EES. In practice, the average first passage time of the reconstructed “orbit” can be used to determine EES. Numerical evidence of these results are given and the application to the mechanical gearbox signal are shown.

scholarly journals Shapes of stationary autocovariances

2006 ◽  
Vol 43 (04) ◽  
pp. 1186-1193
Author(s):  
Robert Lund ◽  
Ying Zhao ◽  
Peter C. Kiessler
Keyword(s):  
Time Series ◽  
Hazard Rate ◽  
Convergence Rates ◽  
Stationary Time Series ◽  
Time Series Models ◽  
Mean Squared Errors ◽  
Geometric Convergence ◽  
One Step ◽  
New Better Than Used ◽  
Better Than

This note introduces shape orderings for stationary time series autocorrelation and partial autocorrelation functions and explores some of their convergence rate ramifications. The shapes explored include decreasing hazard rate and new better than used, orderings that are familiar from stochastic processes settings. Time series models where these shapes arise are presented. The shapes are used to obtain explicit geometric convergence rates for mean squared errors of one-step-ahead forecasts.

scholarly journals Asymptotic Information Measures Discrimination of Non-Stationary Time Series Based on Wavelet Domain

2016 ◽  
Vol 39 (1) ◽  
pp. 81-95
Author(s):  
Behzad Mansouri ◽  
Rahim Chinipa
Keyword(s):  
Time Series ◽  
Real Data ◽  
Stationary Time Series ◽  
Wavelet Domain ◽  
Classification Methods ◽  
Information Measures ◽  
Stationary Time ◽  
Discrimination Information ◽  
Better Than ◽  
Minimum Discrimination Information

<p>This article is concerned with the problem of discrimination between two classes of locally stationary time series based on minimum discrimination information. We view the observed signals as realizations of Gaussian locally stationary wavelet (LSW) processes. The asymptotic Kullback - Leibler discrimination information and Chernoff discrimination information are developed as discriminant criteria for LSW processes. The simulation study showed that our procedure performs as well as other procedures and in some cases better than some other classification methods. Applications to classifying real data show the usefulness of our discriminant criteria.</p>

scholarly journals Shapes of stationary autocovariances

2006 ◽  
Vol 43 (4) ◽  
pp. 1186-1193
Author(s):  
Robert Lund ◽  
Ying Zhao ◽  
Peter C. Kiessler
Keyword(s):  
Time Series ◽  
Hazard Rate ◽  
Convergence Rates ◽  
Stationary Time Series ◽  
Time Series Models ◽  
Mean Squared Errors ◽  
Geometric Convergence ◽  
One Step ◽  
New Better Than Used ◽  
Better Than

This note introduces shape orderings for stationary time series autocorrelation and partial autocorrelation functions and explores some of their convergence rate ramifications. The shapes explored include decreasing hazard rate and new better than used, orderings that are familiar from stochastic processes settings. Time series models where these shapes arise are presented. The shapes are used to obtain explicit geometric convergence rates for mean squared errors of one-step-ahead forecasts.

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