This study extends the modified moving sum statistic (mMOSUM) method to online monitoring variance changes in a linear regression model with long-memory time series errors. Under the null hypothesis, the limit distrib...
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This study extends the modified moving sum statistic (mMOSUM) method to online monitoring variance changes in a linear regression model with long-memory time series errors. Under the null hypothesis, the limit distribution is obtained by modifying the boundary function, and the consistency of the method is proved under the alternative hypothesis. The results of numerical simulation show that the mMOSUM method remains effective when the linear regression model has long-memory time series errors. As the location of the change point moves further back, the effect of the modified method on the increase of the power and the reduction of the run length is more obvious. Finally, we demonstrate the effectiveness of this method through a set of actual data.
Mass fraction of ash is a quality criterion for determining the botanical origin of honey At present, this parameter is generally being replaced by the measurement of electrical conductivity (K). The value K depends o...
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Mass fraction of ash is a quality criterion for determining the botanical origin of honey At present, this parameter is generally being replaced by the measurement of electrical conductivity (K). The value K depends on the ash and acid content of honey;the higher their content, the higher the resulting conductivity A linear regression model for the relationship between ash and electrical conductivity has been established for Slovenian honey by analysing 290 samples of Slovenian honey (including acacia, lime, chestnut, spruce, fir, multifloral and mixed forest honeydew honey). The obtained model differs from the one proposed by the International Honey Commission (IHC) in the slope, but not in the section part of the relation formula. Therefore, the Slovenian model is recommended when calculating the ash mass fraction from the results of electrical conductivity in samples of Slovenian honey.
We constructed six new models to analyze the DNA sequences. First, we regarded a DNA primary sequenceas a random process in t and gave three ways to define nucleotides' random distribution functions. We extracted ...
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We constructed six new models to analyze the DNA sequences. First, we regarded a DNA primary sequenceas a random process in t and gave three ways to define nucleotides' random distribution functions. We extracted some parameters from the linearmodel and analyzed the changes of the nucleotides' distributions. In order to facilitate the comparison of DNA sequences, we proposed two ways to measure their similarities. Finally, we compared the six models by analyzing the similarities of the DNA primary sequences presented in Table 1 and selected the optimal one. (C) 2007 Wiley Periodicals, Inc.
Software cost estimation is always an essential task for the development management as it requires for estimating the effort and the time required for developing the software. A project manager requires software estim...
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ISBN:
(纸本)9781728188676
Software cost estimation is always an essential task for the development management as it requires for estimating the effort and the time required for developing the software. A project manager requires software estimation for making a decision and predict the total budget. Success or failure of software development depends on the accurate estimation of cost and time. There are numerous tools and techniques have been developed for estimating the software cost. But all these techniques are best suitable for the traditional development methodology. From the past two decades, the agile methodology has been com for software development. So the traditional cost estimation techniques may not give the appropriate results for agile development. In this paper, the multiple linear regression models are proposed for comparing the best model for agile development. The correlation between the dependent and independent variables are also found out. The results showed that the proposed model outperforms from the decision tree, stochastic gradient boosting, and random forest.
Noise pollution is the most ignored and underappreciated problem in the world. Even though scientists all over the world have done a lot of research on noise mapping and possible solutions, these solutions are still a...
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Noise pollution is the most ignored and underappreciated problem in the world. Even though scientists all over the world have done a lot of research on noise mapping and possible solutions, these solutions are still a long way from being put into practice. Noise reduction is an important step toward making a community that can last for a long time. Without systematic noise mapping, it is hard to figure out how noise changes in space and time. Using the Norsonic sound level meter, this research provides a novel methodological framework to integrate linear regression models with acoustic propagation for dynamic noise maps in Central Delhi. The 17 most sensitive receptors are also located in the study area. The noise mapping has been performed with the help of Dhwani pro and Arc-GIS software. The results from the noise mapping shows that the study area has noise at hazardous level. The second order linearregression noise prediction model has also been used for prediction of noise levels with taking parameters vehicle flow, % of heavy motor vehicle and light motor vehicle as inputs. The prediction performance is ascertained using the statistical test. The predicted noise values show good correlation with the observed noise levels i.e., R of 0.90. The isolation barriers of 5 m height are also introduced in the noise mapping analysis using Dhwani pro. These barriers represent substantial improvement in the noise level. The overall scenario of noise pollution in the study area is at alarming level and requires immediate planning to control the situation.
Traditional indoor location technologies such as infrared technology and ultrasonic technology are complex, expensive, or having unsatisfactory location accuracy. Radio frequency identification (RFID) technologies are...
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Traditional indoor location technologies such as infrared technology and ultrasonic technology are complex, expensive, or having unsatisfactory location accuracy. Radio frequency identification (RFID) technologies are very popular in many areas since their costs are very low. The tag in such technologies acts as the transmitter, and the radio signal strength indicator (RSSI) information is measured at the reader. However, RSSI information suffers strictly from the multipath circumstance and circumferential elements. Therefore, the localization accuracy of the boundary will be affected severely. In order to solve this problem, we introduce the boundary virtual reference label (BVIRE) algorithm to well utilize RFID techniques for locating the tracking object, which inserts some virtual reference tags on the boundary by establishing a linear regression model that eliminates unwanted tag information from the estimation procedure. The positioning accuracy of the boundary tags and stability have been improved significantly, at least 78%, without adding extra reference tags or radio frequency interference. Also, the estimation errors of our improved BVIRE are much smaller compared to the virtual reference label, location identification based on the dynamic active RFID calibration (LANDMARC), ultrawide band, RADAR, and PinPoint algorithms.
The linear regression model explores the relationship between the dependent variable and the independent variables. The ordinary least squared estimator (OLSE) is widely applicable to estimate the parameters of the mo...
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The linear regression model explores the relationship between the dependent variable and the independent variables. The ordinary least squared estimator (OLSE) is widely applicable to estimate the parameters of the model. However, OLSE suffered a breakdown when the independent variables are linearly dependent- a condition called multicollinearity. The Kibria-Lukman estimator (KLE) was suggested as an alternative to the OLSE and some other estimators (ridge and Liu estimators). In this paper, we developed a Jackknifed version of the Kibria-Lukman estimator- the estimator is named the Jackknifed KL estimator (JKLE). We derived the statistical properties of the new estimator and compared it theoretically with the KLE and some other existing estimators. Theoretically, the result revealed that JKLE possesses the lowest MSE when compared with the KLE and some other existing estimators. Finally, JKLE reduced the bias and the mean squared error (MSE) of KLE in both simulation and real-life analysis. JKLE dominates other methods considered in this study.
The Koul-Susarla-Van Ryzin (KSV) and weighted least squares (WLS) methods are simple to use techniques for handling linear regression models with censored data. They do not require any iterations and standard computer...
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The Koul-Susarla-Van Ryzin (KSV) and weighted least squares (WLS) methods are simple to use techniques for handling linear regression models with censored data. They do not require any iterations and standard computer routines can be employed for model fitting. Emphasis has been given to the consistency and asymptotic normality for both estimators, but the finite sample performance of the WLS estimator has not been thoroughly investigated. The finite sample performance of these two estimators is compared using an extensive simulation study as well as an analysis of the Stanford heart transplant data. The results demonstrate that the WLS approach performs much better than the KSV method and is reliable for use with censored data. (c) 2007 Elsevier B.V. All rights reserved.
An approach is described for viewing the interrelationship between different variables and also tracing the sources of pollution of groundwater of north Chennai (India). The data set of 43 variables which include majo...
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An approach is described for viewing the interrelationship between different variables and also tracing the sources of pollution of groundwater of north Chennai (India). The data set of 43 variables which include major ions, minor ions and trace metal speciation (Cu, Pb, Cd and Zn) collected during the pre-monsoon and post-monsoon seasons of the year 2000-2001, was subjected to R-mode factor analysis to comprehend the distribution pattern of the said variables. It was found that first factor measures salinity and hardness which explained 19.12% of the total variance (comprised of variables EC, TDS, Na+, K+, Ca2+, Mg2+, total hardness, Cl- and SO42-) during pre-monsoon, while it was 25.08% during post-monsoon. The second and third factors were attributed to speciation of zinc and copper ions during both pre-monsoon and post-monsoon. Although there were two more factors, loaded with speciation parameters of lead and cadmium, the variance of them were less than 10%. From this study it is seen that sea water intrusion, municipal solid waste disposal are the identified sources of component of pollution. The importance of metal ions is taking a secondary role and the anthropogenic origin-industrial activity, is the reason in the evaluation of pollution status as they come in the second, third, fourth and fifth factors. As the trace metal speciation was grouped in separate factors, linear regression model (LRM) with correlation analysis was applied to check its validity for prediction of speciation and to apply LRM for rapid monitoring of water pollution.
Interval-censored covariates are sometimes encountered in longitudinal studies and considered as possible predictors in a regressionmodel. This paper, motivated by an AIDS study, proposes an implementation in R for t...
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Interval-censored covariates are sometimes encountered in longitudinal studies and considered as possible predictors in a regressionmodel. This paper, motivated by an AIDS study, proposes an implementation in R for the estimation of parameters and the assessment of the assumptions of a linear regression model with an interval-censored covariate. The properties of the parameters estimators and the behavior of three proposed residuals are addressed through two simulation studies. Also, guidelines are provided to check the goodness-of-fit of the fitted model in terms of the length of the censoring interval of the covariate. The methodology is illustrated with real data coming from the AIDS study. R functions and scripts are provided.
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