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  • Statistics In Transition

 

Article | 06-July-2017

A NEW MEDIAN BASED RATIO ESTIMATOR FOR ESTIMATION OF THE FINITE POPULATION MEAN

The present paper deals with a new median based ratio estimator for the estimation of finite population means in the absence of an auxiliary variable. The bias and mean squared error of the proposed median based ratio estimator are obtained. The performance of the median based ratio estimator is compared with that of the SRSWOR sample mean, ratio estimator and linear regression estimator for certain natural population. It is shown from the numerical comparisons that the proposed median based

J. Subramani

Statistics in Transition New Series, Volume 17 , ISSUE 4, 591–604

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 | 20-December-2020

Some linear regression type ratio exponential estimators for estimating the population mean based on quartile deviation and deciles

This paper deals some linear regression type ratio exponential estimators for estimating the population mean using the known values of quartile deviation and deciles of an auxiliary variable in survey sampling. The expressions of the bias and the mean square error of the suggested estimators have been derived. It was compared with the usual mean, usual ratio (Cochran (1977)), Kadilar and Cingi (2004, 2006) and Subzar et al. (2017) estimators. After comparison, the condition which makes the

Shakti Prasad

Statistics in Transition New Series, Volume 21 , ISSUE 5, 85–98

Article | 28-August-2018

ESTIMATION OF THE SEASONAL DEMAND FOR COOLING BASED ON THE SHORT-TERM DATA

The paper analyzes the possibility of using the energy signature method based on the linear regression to determine the seasonal energy demand for cooling and ventilation in the office building. The “extended” energy signature method (H-m method) was described and applied. In accordance with Standard (EN 15603) the estimation of energy consumption for cooling can be performed for a period shorter than the entire season, but data range must be appropriate to obtain the correct accuracy of the

Dorota BARTOSZ, Aleksandra SPECJAŁ

Architecture, Civil Engineering, Environment, Volume 10 , ISSUE 2, 133–143

Article | 22-July-2019

EFFICIENT TWO-PARAMETER ESTIMATOR IN LINEAR REGRESSION MODEL

Ashok V. Dorugade

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

Article | 06-July-2017

LOCALLY REGULARIZED LINEAR REGRESSION IN THE VALUATION OF REAL ESTATE

Mariusz Kubus

Statistics in Transition New Series, Volume 17 , ISSUE 3, 515–524

Article | 06-July-2017

INFORMATIVE VERSUS NON-INFORMATIVE PRIOR DISTRIBUTIONS AND THEIR IMPACT ON THE ACCURACY OF BAYESIAN INFERENCE

In this study the benefits arising from the use of the Bayesian approach to predictive modelling will be outlined and exemplified by a linear regression model and a logistic regression model. The impact of informative and non-informative prior on model accuracy will be examined and compared. The data from the Central Statistical Office of Poland describing unemployment in individual districts in Poland will be used. Markov Chain Monte Carlo methods (MCMC) will be employed in modelling.

Wioletta Grzenda

Statistics in Transition New Series, Volume 17 , ISSUE 4, 763–780

Article | 05-September-2020

FACTORS THAT INFLUENCE LOGISTICS DECISION MAKING IN THE SUPPLY CHAIN OF THE AUTOMOTIVE INDUSTRY

in the organization of logistics processes. For this purpose, a statistical analysis was carried out on a sample of companies from the Slovenian automotive industry, which is one of the leading high-tech industries in the world. The results of multiple linear regression show that the greater the knowledge of logistics among the employees from other departments, the more logistics costs are taken into account during the development of the product. This is an important finding for the automotive

Sebastjan ŠKERLIČ

Transport Problems, Volume 15 , ISSUE 3, 117–126

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

Research paper | 15-January-2019

A relationship between brainstem auditory evoked potential and vagal control of heart rate in adult women

an audiometry examination, followed by rest for 10 minutes for HR recording. Next, ABR evaluation was completed discretely in both ears, with I, III and V wave components. Linear regression revealed that the root-mean square of differences between adjacent normal RR intervals (RMSSD) and the triangular interpolation of RR interval (TINN) exhibited a significant association with Wave I in the right ear. These variables contributed to 28.2% (R2) of Wave I. In conclusion, there was a significant

Ariany G. Silva, Ana Claúdia F. Frizzo, Eduardo F. B. Chagas, David M. Garner, Rodrigo D. Raimundo, Luiz Vinicius de Alcantara Sousa, Vitor E. Valenti

Acta Neurobiologiae Experimentalis, Volume 78 , ISSUE 4, 305–314

Research paper | 01-November-2017

BORROWING INFORMATION OVER TIME IN BINOMIAL/LOGIT NORMAL MODELS FOR SMALL AREA ESTIMATION

Linear area level models for small area estimation, such as the Fay-Herriot model, face challenges when applied to discrete survey data. Such data commonly arise as direct survey estimates of the number of persons possessing some characteristic, such as the number of persons in poverty. For such applications, we examine a binomial/logit normal (BLN) model that assumes a binomial distribution for rescaled survey estimates and a normal distribution with a linear regression mean function for

Carolina Franco, William R. Bell

Statistics in Transition New Series, Volume 16 , ISSUE 4, 563–584

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

research-article | 30-November-2018

Impact of Globodera ellingtonae on yield of potato (Solanum tuberosum)

tuber was weighed individually. A two-way analysis of variance (ANOVA) with trial, Pi and trial × Pi was used to test for difference in mean yields, aboveground biomass, tuber number, and individual tuber weight, followed by a Tukey’s honest significant difference (HSD) test for pairwise comparisons; since trial was not significant in the model, data from the trials were combined for presentation. To test for a relationship between Pi and tuber yield and Pi and aboveground biomass, linear regression

Inga A. Zasada, Russell E. Ingham, Hannah Baker, Wendy S. Phillips

journal of nematology, Volume 51 , 1–10

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