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  • Statistics In Transition

 

Research Article | 08-December-2021

Estimation of the density and cumulative distribution functions of the exponentiated Burr XII distribution

The exponentiated Burr Type XII (EBXII) distribution has wide applications in reliability and economic studies. In this article, the estimation of the probability density function and the cumulative distribution function of EBXII distribution is considered. We examine the maximum likelihood estimator, the uniformly minimum variance unbiased estimator, the least squares estimator, the weighted least squares estimator, the maximum product spacing estimator, the Cramér–von-Mises

Amal S. Hassan, Salwa M. Assar, Kareem A. Ali, Heba F. Nagy

Statistics in Transition New Series, Volume 22 , ISSUE 4, 171–189

Research Article | 13-December-2019

GENERAL LINEAR MODEL: AN EFFECTIVE TOOL FOR ANALYSIS OF CLAIM SEVERITY IN MOTOR THIRD PARTY LIABILITY INSURANCE

The paper focuses on the analysis of claim severity in motor third party liability insurance under the general linear model. The general linear model combines the analyses of variance and regression and makes it possible to measure the influence of categorical factors as well as the numerical explanatory variables on the target variable. In the paper, simple, main and interaction effects of relevant factors have been quantified using estimated regression coefficients and least squares means

Erik Šoltés, Silvia Zelinová, Mária Bilíková

Statistics in Transition New Series, Volume 20 , ISSUE 4, 13–31

Article | 05-September-2021

A new reciprocal Rayleigh extension: properties, copulas, different methods of estimation and a modified right-censored test for validation

In this article, a new reciprocal Rayleigh extension called the Xgamma reciprocal Rayleigh model is defined and studied. The relevant statistical properties are derived, and the useful results related to the convexity and concavity are addressed. We discussed the estimation of the parameters using different estimation methods such as the maximum likelihood estimation method, the ordinary least squares estimation method, the weighted least squares estimation method, the Cramer-Von-Mises

Haitham M. Yousof, M. Masoom Ali, Hafida Goual, Mohamed Ibrahim

Statistics in Transition New Series, Volume 22 , ISSUE 3, 99–121

Research paper | 10-April-2013

STUDY ON FEATURE SELECTION AND IDENTIFICATION METHOD OF TOOL WEAR STATES BASED ON SVM

, Least Squares Support Vector Machines (LS-SVM) is introduced. In order to estimate the effectiveness of feature selection algorithm, the comparative analysis among Fisher Score (FS) Information Gain (IG) and SVM-RFE is exploited to real milling datasets. The identification result proves that: The selected feature set based on SVM-RFE is more effective to recognize tool wear state; LS-SVM wear identification method is superior to BP neural network, and it has higher identification accuracy; the

Weilin Li, Pan Fu, Weiqing Cao

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 2, 448–465

Research Article | 13-December-2018

DEALING WITH HETEROSKEDASTICITY WITHIN  THE MODELING OF THE QUALITY OF LIFE  OF OLDER PEOPLE

Using the estimation method of ordinary least squares leads to unreliable results in the case of heteroskedastic linear regression model. Other estimation methods are described, including weighted least squares, division of the sample and heteroskedasticity-consistent covariance matrix estimators, all of which can give estimators with better properties than ordinary least squares. The methods are presented giving the example of modelling quality of life of older people, based on a data set from

Katarzyna Jabłońska

Statistics in Transition New Series, Volume 19 , ISSUE 3, 423–452

Article | 26-May-2019

AN ALTERNATIVE MATRIX TRANSFORMATION TO THE F TEST STATISTIC FOR CLUSTERED DATA

squares procedures for the regression coefficients hypothesis. By Monte Carlo simulations of the balanced and unbalanced data, it is found that the F test statistic based on generalized least squares procedures with Adjusted Householder transformation performs well in terms of the type I error rate and power of the test.

Sukanya Intarapak, Thidaporn Supapakorn

Statistics in Transition New Series, Volume 20 , ISSUE 1, 153–174

Research Article | 27-December-2017

Hybrid Intelligent Method of Identifying Stator Resistance of Motorized Spindle

Aiming at the problem that changes of nonlinear dynamic resistance of stator affect the performance of speed sensorless vector control system, a hybrid computing intelligence approach is used in the identification of stator resistance of motorized spindle. The partial least squares (PLS) regression is combined with neural network to solve the problem of few samples and multi-correlation of variables in complicated data modeling. The PLS method is used to extract variable components from sample

Lixiu Zhang, Yuhou Wu, Ke Zhang

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 2, 781–797

Article | 28-July-2018

GENERALIZED EXPONENTIAL SMOOTHING IN PREDICTION OF HIERARCHICAL TIME SERIES

Shang and Hyndman (2017) proposed a grouped functional time series forecasting approach as a combination of individual forecasts obtained using the generalized least squares method. We modify their methodology using a generalized exponential smoothing technique for the most disaggregated functional time series in order to obtain a more robust predictor. We discuss some properties of our proposals based on the results obtained via simulation studies and analysis of real data related to the

Daniel Kosiorowski, Dominik Mielczarek, Jerzy P. Rydlewski, Małgorzata Snarska

Statistics in Transition New Series, Volume 19 , ISSUE 2, 331–350

Article | 17-July-2017

On the Performance of Some Biased Estimators in a Misspecified Model with Correlated Regressors

Abstract In this paper, the effect of misspecification due to omission of relevant variables on the dominance of the r − (k, d) class estimator proposed by Özkale (2012), over the ordinary least squares (OLS) estimator and some other competing estimators when some of the regressors in the linear regression model are correlated, have been studied with respect to the mean squared error criterion. A simulation study and numerical example have been demostrated to compare the performance of the

Shalini Chandra, Gargi Tyagi

Statistics in Transition New Series, Volume 18 , ISSUE 1, 27–52

Article | 22-July-2019

EFFICIENT TWO-PARAMETER ESTIMATOR IN LINEAR REGRESSION MODEL

In this article, two-parameter estimators in linear model with multicollinearity are considered. An alternative efficient two-parameter estimator is proposed and its properties are examined. Furthermore, this was compared with the ordinary least squares (OLS) estimator and ordinary ridge regression (ORR) estimators. Also, using the mean squares error criterion the proposed estimator performs more efficiently than OLS estimator, ORR estimator and other reviewed two-parameter estimators. A

Ashok V. Dorugade

Statistics in Transition New Series, Volume 20 , ISSUE 2, 173–185

Article | 06-July-2017

MODELLING ROAD TRAFFIC CRASHES USING SPATIAL AUTOREGRESSIVE MODEL WITH ADDITIONAL ENDOGENOUS VARIABLE

Olusanya Elisa Olubusoye, Grace Oluwatoyin Korter, Afees Adebare Salisu

Statistics in Transition New Series, Volume 17 , ISSUE 4, 659–670

Research Article | 13-December-2017

RAILWAY WHEELSET PARAMETER ESTIMATION USING SIGNALS FROM LATERAL VELOCITY SENSOR

) element to reduce estimation bias and a variable forgetting factor for good parameter tracking and smooth steady state. Simulation results have shown that the LAE with fixed forgetting factor gives better parameter estimates compared to the recursive least-squares error (RLSE) method, whereas the LAE+VFF offers even better estimation and tracking of system parameters that are subject to abrupt changes, provided that the fs and lf values are chosen accordingly. It has also been proven that the

H. Selamat, A. J. Alimin, Y. M. Sam

International Journal on Smart Sensing and Intelligent Systems, Volume 1 , ISSUE 3, 754–770

Article | 01-December-2016

OPTIMAL ANALOG WAVELET BASES CONSTRUCTION USING HYBRID OPTIMIZATION ALGORITHM

An approach for the construction of optimal analog wavelet bases is presented. First, the definition of an analog wavelet is given. Based on the definition and the least-squares error criterion, a general framework for designing optimal analog wavelet bases is established, which is one of difficult nonlinear constrained optimization problems. Then, to solve this problem, a hybrid algorithm by combining chaotic map particle swarm optimization (CPSO) with local sequential quadratic programming

Hongmin Li, Yigang He, Yichuang Sun

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 4, 1918–1942

Article | 01-March-2015

SMALL TARGET DETECTION BASED ON INFRARED IMAGE ADAPTIVE

object extraction methods such as recursive least squares method, and the experimental results show that the algorithm capable of detecting small targets in infrared images, with good results. based pretreatment small target motion continuity characteristics studied image sequences small moving target detection algorithm based on weighted dynamic programming, dynamic programming algorithm principle is given, analysis of the direct method and the accumulation of gray dynamic programming algorithm

Hao Chen, Hong Zhang, Yifan Yang, Ding Yuan

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 1, 497–515

Article | 20-December-2020

Modelling bid-ask spread conditional distributions using hierarchical correlation reconstruction

, we first normalized marginal distributions so that they were nearly uniform. Then we modelled joint densities as linear combinations of orthonormal polynomials, obtaining their decomposition into mixed moments. Then we modelled each moment of the predicted variable separately as a linear combination of mixed moments of known variables using least squares linear regression. By combining these predicted moments, we obtained the predicted density as a polynomial, for which we can e.g. calculate the

Jarosław Duda, Henryk Gurgul, Robert Syrek

Statistics in Transition New Series, Volume 21 , ISSUE 5, 99–118

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