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Research & Reviews : Journal of Statistics
Number of Followers: 6  

  This is an Open Access Journal Open Access journal
ISSN (Print) 2348-7909 - ISSN (Online) 2278-2273
Published by STM Journals Homepage  [36 journals]
  • Empirical Study on Gini Ratio for left Truncated Pareto Model

    • Authors: G. Venkatesan, R. Selvam, V. Ayyandurai
      Pages: 1 - 4s
      Abstract: In this paper, the Gini’s concentration ratio is used to find the variability in district size distribution based on the Pareto model. We studied the variability of the male and female rural population of all districts in Tamil Nadu state as per 2011 census data using the Gini’s concentration ratio based on Pareto distribution and it is found that the Gini’s concentration ratio for female population is greater than the male population. This indicates that the female population has more concentration than male population in district level of the state. Also it is found that the Gini’s concentration ratio is truncated invariant only for the Pareto distribution. Keywords: District size distribution, Gini’s concentration ratio, invariant, maximum likelihood estimation, Pareto model, truncation-invariantCite this ArticleVenkatesan G, SelvamR, Ayyandurai V. Empirical Study on Gini Ratio for Left Truncated Pareto Model. Research & Reviews: Journal of Statistics. 2018; 7(1): 1s–4sp.
      PubDate: 2018-05-17
      Issue No: Vol. 7, No. 1 (2018)
       
  • Acceptance Single Sampling Plan Based on Truncated Life Tests for the
           Weibull-Poisson Distribution

    • Authors: V. Kaviyarasu, P. Fawaz
      Pages: 5 - 11s
      Abstract: In this paper a new distribution is used to design the plan parameters of acceptance single sampling plan through percentiles of Weibull-Poisson distribution. The life of the product is assumed to follow Weibull-Poisson distribution. The minimum sample size satisfying the specified levels of consumer’s risk and producer’s risk are obtained. The operating characteristic values are given and the ratio dq is obtained which ensures the producer’s risk at 5% level. Keywords: Reliability sampling plan, Weibull-Poisson distribution, percentilesCite this ArticleKaviyarasu V, Fawaz P. Acceptance Single Sampling Plan based on Truncated Life tests for the Weibull-Poisson Distribution. Research & Reviews: Journal of Statistics. 2018; 7(1): 5s–11sp.
      PubDate: 2018-05-17
      Issue No: Vol. 7, No. 1 (2018)
       
  • Bayesian Analysis of Operating Characteristic Curves of Single Sampling
           Plans by Attributes under Mixture of Two Binomial Distributions

    • Authors: M. Saral, A. Loganathan, P. Muthu Krishnan, R. Vijaya Raghavan
      Pages: 12 - 23s
      Abstract: In the current scenario, manufacturing of products in single stream is not enough to meet the needs of consumers and in order to increase the production, two or more streams are required and the lots are formed from various streams. The production unit differs due to input raw materials, machineries, manpower and management, there may be inherent variation among the quality of the products manufactured from different streams. In this situation, the simple probability distribution is not appropriate and instead, finite mixture distribution is more appropriate. A finite mixture distribution is an appropriate probability distribution to study the product quality of such heterogeneous lots. In this paper, the operating characteristic (OC) function for Bayesian single sampling plans by attributes under the conditions of mixture of two binomial distributions is derived. The OC curves are drawn for different sets of parameters. Properties of the OC function with reference to the plan parameters are studied from the empirical analysis of the behaviour of the OC curves. Keywords: Single sampling plan, mixture of two binomial distributions, operating characteristic function, producer quality level, consumer quality level, producer’s risk, consumer’s risk, prior distributionCite this ArticleSaral M, Loganathan A, Muthu Krishnan P, et al. Bayesian Analysis of Operating Characteristic Curves of Single Sampling Plans by Attributes under Mixture of Two Binomial Distributions. Research & Reviews: Journal of Statistics. 2018; 7(1): 12s–23sp.
      PubDate: 2018-05-17
      Issue No: Vol. 7, No. 1 (2018)
       
  • Bayesian Weibull Model in Survival Analysis

    • Authors: G. Venkatesan, P. Saranya
      Pages: 24 - 29s
      Abstract: In this paper, we examine the performance of Maximum Likelihood Estimation (MLE) and Bayesian estimation of survival function of Weibull distribution with two parameters to the assigning informative prior and with three loss functions, namely, Linear Exponential (LINEX) Loss, General Entropy Loss and Squared Error Loss. Here the simulation study is carried out and the statistical analysis is done. Keywords: Weibull distribution, maximum likelihood estimation, Bayesian estimation, linear exponential loss, general entropy loss and squared error loss, Lindley approximation Cite this ArticleVenkatesan G, Saranya P. Bayesian Weibull Model in Survival Analysis. Research & Reviews: Journal of Statistics. 2018; 7(1): 24s–29sp.
      PubDate: 2018-05-17
      Issue No: Vol. 7, No. 1 (2018)
       
  • Data Mining Methods for Large Data

    • Authors: P. Arumugam, P. Jose
      Pages: 30 - 35s
      Abstract: High dimensional data classification becomes a challenging task because data are large, complex to handle, heterogeneous and hierarchical. In order to reduce the data set without affecting the classifier accuracy, the feature selection plays a vital role in large datasets and which increases the efficiency of classification to choose the important features for high dimensional classification, when those features are irrelevant or correlated. Therefore feature selection is considered to be used in preprocessing before applying classifier to a data set. Thus this good choice of feature selection leads to the high classification accuracy and minimizing computational cost. Though different kinds of feature selection methods are investigated for selecting and fitting features, the best algorithm should be preferred to maximize the accuracy of the classification. In this paper, initial subset selection is based on the integration of PSO and DT. The novel approach aimed to speed up the training time and optimize the SVM classifier accuracy automatically. The proposed model is used to select minimum number of features and providing high classification accuracy of large datasets. Keywords: Feature selection, decision tree, classification, PSO, SVMCite this ArticleArumugam P, Jose P. Data Mining Methods for Large Data. Research & Reviews: Journal of Statistics. 2018; 7(1): 30s–35sp.
      PubDate: 2018-05-17
      Issue No: Vol. 7, No. 1 (2018)
       
  • Designing Reliability Single Sampling Plans based on Exponentiated
           Rayleigh Distribution

    • Authors: A. Loganathan, M. Gunasekaran
      Pages: 36 - 40s
      Abstract: Reliability sampling plans are used in manufacturing industries to take decision on the disposition of lots of finished products based on the information obtained from concerned life test. Among various schemes, hybrid censoring scheme is applied with an objective of saving both time and cost of inspecting the products simultaneously. Parameters of the reliability sampling plans under hybrid censoring scheme are determined corresponding to the lifetime distribution. Exponential, Weibull, Rayleigh and log-normal distributions are some of the distributions often used in reliability and life testing. Exponentiated Rayleigh distribution is obtained by introducing a new parameter in the exponent of the cumulative distribution function of the Rayleigh distribution. Exponentiation enables to make a precise choice of the distribution. This paper attempts to design reliability (single) sampling plans under hybrid censoring scheme assuming that the life time of the product follows exponentiated Rayleigh distribution. Plan parameters are obtained corresponding to the specified points on the operating characteristic curve. Selection of the plan parameters is illustrated through numerical example. Keywords: Reliability sampling plan, hybrid censoring, exponentiated Rayleigh distribution, producer’s risk, consumer’s riskCite this ArticleLoganathan A, Gunasekaran M. Designing Reliability Single Sampling Plans based on Exponentiated Rayleigh Distribution. Research & Reviews: Journal of Statistics. 2018; 7(1): 36s–40sp.
      PubDate: 2018-05-17
      Issue No: Vol. 7, No. 1 (2018)
       
  • Dual Principal Component Analysis in Software Reliability Studies

    • Authors: A. Loganathan, R. Jeromia Muthuraj
      Pages: 41 - 46s
      Abstract: Software usage has been occupying major parts in all the activities of individuals as well as organizations. Expectation of the end users about failure free operations of software is also increasing rapidly. Zhang and Pham defined 32 environmental factors for studying reliability of software and categorized them into five groups. Later Zhu et al. proposed to use information about three principal components extracted from 10 environmental factors only to study software reliability. It causes loss of information about the remaining 22 factors; two more environmental factors have been recommended as significant factors in a subsequent literature for studying reliability of software. This paper proposes a methodology to use the information about all the 34 factors through principal components, reducing the volume of information with relatively less amount of loss of information. Principal components are extracted in two-stage grouping of the 34 environmental factors. Information gained from the different stages of PCs is compared with Shannon information measure. Keywords: Software reliability, environmental factor, clustering, principal components, Shannon entropyCite this ArticleLoganathan A, Jeromia Muthuraj R. Dual Principal Component Analysis in Software Reliability Studies. Research & Reviews: Journal of Statistics. 2018; 7(1): 41s–46sp.
      PubDate: 2018-05-17
      Issue No: Vol. 7, No. 1 (2018)
       
  • Least Squares and Weighted Least Squares Median Rank Estimation Method for
           an Inverse Weibull Distribution for Progressively Type-II Censored Samples
           

    • Authors: A. Loganathan, M. Uma
      Pages: 47 - 52s
      Abstract: Inverse Weibull distribution is non-monotonic and unimodal hazard function distribution having wider application in reliability and life testing. Reciprocal of a random variable distributed according to the Weibull distribution has the inverse Weibull distribution. This paper attempts to study the classical approach for parameter estimation of inverse Weibull distribution under progressive type II censoring. Least square median rank estimator and weighted least square median rank estimator are derived, it admits closed form expression. An extensive simulation study compares the performance of these estimators. Lastly, a real data example has been provided for illustrative purposes.  Keywords: Inverse Weibull distribution, progressive type II censoring, least square median rank estimation, weighted least square median rank estimation, Monte Carlo simulationCite this ArticleA. Loganathan, M. Uma. Least Squares and Weighted Least Squares Median Rank Estimation Method for an Inverse Weibull Distribution for Progressively Type-II Censored Samples. Research & Reviews: Journal of Statistics. 2018; 7(1): 47s–52sp
      PubDate: 2018-05-17
      Issue No: Vol. 7, No. 1 (2018)
       
  • Monitoring Process Performance using Multivariate Control Chart

    • Authors: R. Sasikumar, S. Bangusha Devi
      Pages: 53 - 56s
      Abstract: Statistical Process Control (SPC) is the most important procedure and decision making method which allows us to see when a process is working properly and when it is not. Walter A. Shewhart developed the concept of control chart in 1920’s, which provides a simple way to determine if the process is in control or not. Variation is present in any process, deciding when the variation is natural and when it needs correction is the key to quality control. Traditional SPC methodologies are not suitable to monitor and control multiple variables while one variable is correlated with other variables. Further, univariate control charts are difficult to manage and analyze because of the large numbers of control charts of each process variable. Multivariate analyses utilize the additional information due to the relationships among the variables and these concepts may be used to develop more efficient control charts than the simultaneous operation of several univariate control charts. Multivariate control chart for process mean is based upon Hotelling's T2 distribution, which was introduced by Hotelling (1947). Multivariate surveillance is of interest in industrial production, for example, in order to monitor several sources of variation in assembled products. This paper studies the application of Multivariate Statistical Process Control (MSPC) charts to monitor yarn quality monitoring production process in a textile industry.  Keywords: Statistical process control, control chart, Shewhart control chart, multivariate control chartCite this ArticleSasikumar R, Bangusha Devi S. Monitoring Process Performance Using Multivariate Control Chart. Research & Reviews: Journal of Statistics. 2018; 7(1): 53s–56sp.
      PubDate: 2018-05-17
      Issue No: Vol. 7, No. 1 (2018)
       
  • Object Recognition by Multi-Scale Color Local Binary Pattern

    • Authors: R. Sunder, Nagalinga Rajan A.
      Pages: 57 - 61s
      Abstract: This paper presents a multi-scale color local binary pattern based method for object recognition. Several advanced methods have been proposed for object recognition using deep neural networks. Although these methods offer high accuracy with many number of object classes, the high complexity of these methods requires large number of training examples and computational resources. This paper attempts to solve the object recognition problem for limited number of well known object classes to be used in practical scenarios. Color features are susceptible to change in varying illumination conditions. Shape features vary with the view angle. However texture features are more dependable. Multi-scale color local binary patterns are computed and artificial neural networks are trained. Experimental results on CALTECH 101, COIL 100 datasets indicate that the proposed method has good performance with low computational complexity.  Keywords: Object recognition, neural networks, classification, texture, local binary pattern Cite this ArticleSunder R, Nagalinga Rajan A. Object Recognition by Multi-Scale Color Local Binary Pattern. Research & Reviews: Journal of Statistics. 2018; 7(1): 57s–61sp.
      PubDate: 2018-05-17
      Issue No: Vol. 7, No. 1 (2018)
       
  • Principal Component Analysis for Outlier Detection

    • Authors: S. Stephen Raj, K. Senthamarai Kannan, K. Manoj
      Pages: 62 - 68s
      Abstract: Principal Components Analysis (PCA) is one of the well-known and frequently used multivariate exploratory data analyses. PCA is concerned with analyzing and understanding data in high dimensions, that is to say, PCA method analyzes data sets that represent observations which are identified by several dependent variables that are inter-correlated. Multidimensional scaling (MDS) is a technique that makes a map displaying the comparative attitudes of a number of objects or cases, which corresponds to the table of distances between them. The process of identifying outliers is an interesting and important aspect in the analysis of data. The objective of this work is to establish the effects of the outliers in PCA for multidimensional scaling techniques and the computed results are obtained from the simulated data. Keywords: Outliers, PCA, MDS, multivariate analysis, robust statisticsCite this ArticleStephen Raj S, Senthamarai Kannan K, Manoj K. Principal Component Analysis for Outlier Detection. Research & Reviews: Journal of Statistics. 2018; 7(1): 62s–68sp.
      PubDate: 2018-05-17
      Issue No: Vol. 7, No. 1 (2018)
       
  • Robust Regression Models for Identifying Outliers in Medical Data

    • Authors: G. Sampath, K. Senthamarai Kannan, K. Manoj
      Pages: 69 - 76s
      Abstract: Before applying any multivariate statistical analysis, it is important to determine whether outliers are present in the dataset. In regression analysis, the presence of outliers in the dataset can strongly mislead the classical least squares estimator and lead to unreliable results. In this paper, we prove the minimum covariance determinant estimator, which is commonly applied in a robust statistic to estimate location parameters and multivariate scales. These ideas can be used to robust distance of Sign method, Mahalanobis distances and Cook’s distances to identify outliers. The intent of this robust regression study is to provide a behavior of outliers in linear regression and to compare robust regression methods. Keywords: Robust distance, sign method, Cook’s distance, outliersCite this ArticleSampath G, Senthamarai Kannan K, Manoj K. Robust Regression Models for Identifying Outliers in Medical Data. Research & Reviews: Journal of Statistics. 2018; 7(1): 69s–76sp
      PubDate: 2018-05-17
      Issue No: Vol. 7, No. 1 (2018)
       
  • Statistical Impact of Climate Variability and Changes on Air Pollution
           Related to Respiratory Diseases

    • Authors: R. Sasikumar, S. Raguraman
      Pages: 77 - 84s
      Abstract: Respiratory diseases constitute one of the leading causes of ill health for both adults and children worldwide. This disease is the first point of contact with air pollutants. 175 hospital admissions with respiratory diseases during May 2016 to October 2016 in Southern districts Tamil Nadu were taken into account for this research work. The data were analyzed with multiple logistic regression analysis by using R software. The aim of this study is to estimate the associations between air pollutants, weather variables and hospital admissions for respiratory diseases in southern districts. In this paper, we observed that the effect of air pollution exposure, particularly SO2, NO2 and PM10 increase the number of hospital admissions for respiratory diseases. Keywords: Air pollutants, weather variables, respiratory diseases, multiple logistic regression Cite this ArticleSasikumar R, Raguraman S. Statistical Impact of Climate Variability and Changes on Air Pollution Related to Respiratory Diseases. Research & Reviews: Journal of Statistics. 2018; 7(1): 77s–84sp.
      PubDate: 2018-05-17
      Issue No: Vol. 7, No. 1 (2018)
       
  • Time Series Model for National Stock Price Prediction

    • Authors: A. Mohamed Ashik, K. Senthamarai Kannan
      Pages: 85 - 90s
      Abstract: The national stock exchange is widest and fully automatic trading system in India. Analysis and prediction of stock market time series data have involved considerable interest from the researchers over the last decade. In this paper, the Nifty 50 closing stock market prices were computed and predicted the trend of stock market fluctuations using time series modeling techniques, like exponential smoothing and autoregressive integrated moving average. The forecasted values of Nifty 50 closing stock price are computed for both models separately and also compared the error rates. From the results, the autoregressive integrated moving average model performed better than the other one model. Keywords: Stock market, national stock exchange, exponential smoothing model, autoregressive integrated moving average model, error rates Cite this ArticleMohamed Ashik A, Senthamarai Kannan K. Time Series Model for National Stock Price Prediction. Research & Reviews: Journal of Statistics. 2018; 7(1): 85s–90sp.
      PubDate: 2018-05-17
      Issue No: Vol. 7, No. 1 (2018)
       
  • Forecasting Maize Price using Exponential Smoothing Method

    • Authors: M. Velusamy, K. Senthamarai Kannan
      Pages: 91 - 95s
      Abstract: Maize is emerging as third most important crop after rice and wheat in India. A better understanding of maize price situation and future prices will facilitate farmers and end users to make appropriate decisions regarding buying and selling patterns; hence government should make adequate policies. Exponential smoothing method is appropriate tool in time series analysis. Forecast of maize price can be done by using simple exponential smoothing method. The forecasted results suggest that there are expectations of increasing maize prices. This method is carried out for statistical model for the time series data. The forecasting accuracy of exponential smoothing method was discussed and computations of root mean square error, mean square error, mean absolute percentage error and mean absolute deviation were done. Forecast using actual values of the data and forecast error rate are discussed numerically and graphically.  Keywords: Maize price, simple exponential smoothing, forecasting, error methodCite this ArticleVelusamy M, Senthamarai Kannan K. Forecasting Maize Price using Exponential Smoothing Method. Research & Reviews: Journal of Statistics. 2018; 7(1): 91s–95sp
      PubDate: 2018-05-17
      Issue No: Vol. 7, No. 1 (2018)
       
 
 
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