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Articles 1 - 14 of 14
Full-Text Articles in Statistics and Probability
Estimation Of Finite Population Mean By Using Minimum And Maximum Values In Stratified Random Sampling, Umer Daraz, Javid Shabbir, Hina Khan
Estimation Of Finite Population Mean By Using Minimum And Maximum Values In Stratified Random Sampling, Umer Daraz, Javid Shabbir, Hina Khan
Journal of Modern Applied Statistical Methods
In this paper we have suggested an improved class of ratio type estimators in estimating the finite population mean when information on minimum and maximum values of the auxiliary variable is known. The properties of the suggested class of estimators in terms of bias and mean square error are obtained up to first order of approximation. Two data sets are used for efficiency comparisons.
A New Estimator Based On Auxiliary Information Through Quantitative Randomized Response Techniques, Nilgün Özgül, Hülya Çıngı
A New Estimator Based On Auxiliary Information Through Quantitative Randomized Response Techniques, Nilgün Özgül, Hülya Çıngı
Journal of Modern Applied Statistical Methods
An exponential-type estimator is developed for the population mean of the sensitive study variable based on various Randomized Response Techniques (RRT) using a non-sensitive auxiliary variable. The mean squared error (MSE) of the proposed estimator is derived for generalized RRT models. The proposed estimator is compared with competitors in a simulation study and an application. The proposed estimator is found to be more efficient using a non-sensitive auxiliary variable.
Effective Estimation Strategy Of Finite Population Variance Using Multi-Auxiliary Variables In Double Sampling, Reba Maji, G. N. Singh, Arnab Bandyopadhyay
Effective Estimation Strategy Of Finite Population Variance Using Multi-Auxiliary Variables In Double Sampling, Reba Maji, G. N. Singh, Arnab Bandyopadhyay
Journal of Modern Applied Statistical Methods
Estimation of population variance in two-phase (double) sampling is considered using information on multiple auxiliary variables. An unbiased estimator is proposed and its properties are studied under two different structures. The superiority of the suggested estimator over some contemporary estimators of population variance was established through empirical studies from a natural and an artificially generated dataset.
Efficient And Unbiased Estimation Procedure Of Population Mean In Two-Phase Sampling, Reba Maji, Arnab Bandyopadhyay, G. N. Singh
Efficient And Unbiased Estimation Procedure Of Population Mean In Two-Phase Sampling, Reba Maji, Arnab Bandyopadhyay, G. N. Singh
Journal of Modern Applied Statistical Methods
In this paper, an unbiased regression-ratio type estimator has been developed for estimating the population mean using two auxiliary variables in double sampling. Its properties are studied under two different cases. Empirical studies and graphical simulation have been done to demonstrate the efficiency of the proposed estimator over other estimators.
A New Exponential Type Estimator For The Population Mean In Simple Random Sampling, Gamze Özel Kadilar
A New Exponential Type Estimator For The Population Mean In Simple Random Sampling, Gamze Özel Kadilar
Journal of Modern Applied Statistical Methods
This paper provides a new exponential type estimator in simple random sampling for population mean. It is shown that proposed exponential type estimator is always more efficient than estimators considered by Bahl and Tuteja (1991) and Singh, Chauhan, Sawan, and Smarandache (2009). From numerical examples it is also observed that proposed modified ratio estimator performs better than existing estimators.
Monte Carlo Comparison Of The Parameter Estimation Methods For The Two-Parameter Gumbel Distribution, Demet Aydin, Birdal Şenoğlu
Monte Carlo Comparison Of The Parameter Estimation Methods For The Two-Parameter Gumbel Distribution, Demet Aydin, Birdal Şenoğlu
Journal of Modern Applied Statistical Methods
The performances of the seven different parameter estimation methods for the Gumbel distribution are compared with numerical simulations. Estimation methods used in this study are the method of moments (ME), the method of maximum likelihood (ML), the method of modified maximum likelihood (MML), the method of least squares (LS), the method of weighted least squares (WLS), the method of percentile (PE) and the method of probability weighted moments (PWM). Performance of the estimators is compared with respect to their biases, MSE and deficiency (Def) values via Monte-Carlo simulation. A Monte Carlo Simulation study showed that the method of PWM was …
A General Procedure Of Estimating Population Mean Using Information On Auxiliary Attribute, Sachin Malik, Rajesh Singh, Florentin Smarandache
A General Procedure Of Estimating Population Mean Using Information On Auxiliary Attribute, Sachin Malik, Rajesh Singh, Florentin Smarandache
Branch Mathematics and Statistics Faculty and Staff Publications
This paper deals with the problem of estimating the finite population mean when some information on auxiliary attribute is available. It is shown that the proposed estimator is more efficient than the usual mean estimator and other existing estimators. The results have been illustrated numerically by taking empirical population considered in the literature.
A Generalized Class Of Estimators For Finite Population Variance In Presence Of Measurement Errors, Prayas Sharma, Rajesh Singh
A Generalized Class Of Estimators For Finite Population Variance In Presence Of Measurement Errors, Prayas Sharma, Rajesh Singh
Journal of Modern Applied Statistical Methods
The problem of estimating the population variance is presented using auxiliary information in the presence of measurement errors. The estimators in this article use auxiliary information to improve efficiency and assume that measurement error is present both in study and auxiliary variable. A numerical study is carried out to compare the performance of the proposed estimator with other estimators and the variance per unit estimator in the presence of measurement errors.
Nonlinear Trigonometric Transformation Time Series Modeling, K. A. Bashiru, O. E. Olowofeso, S. A. Owabumoye
Nonlinear Trigonometric Transformation Time Series Modeling, K. A. Bashiru, O. E. Olowofeso, S. A. Owabumoye
Journal of Modern Applied Statistical Methods
The nonlinear trigonometric transformation and augmented nonlinear trigonometric transformation with a polynomial of order two was examined. The two models were tested and compared using daily mean temperatures for 6 major towns in Nigeria with different rates of missing values. The results were used to determine the consistency and efficiency of the models formulated.
The Bootstrap Method For The Selection Of A Shrinkage Factor In Two-Stage Estimation Of The Reliability Function Of An Exponential Distribution, Makarand V. Ratnaparkhi, Vasant B. Waikar, Fredrick J. Schuurmann
The Bootstrap Method For The Selection Of A Shrinkage Factor In Two-Stage Estimation Of The Reliability Function Of An Exponential Distribution, Makarand V. Ratnaparkhi, Vasant B. Waikar, Fredrick J. Schuurmann
Journal of Modern Applied Statistical Methods
An application of a bootstrap method for selecting a suitable shrinkage factor for the two-stage shrinkage estimator of a reliability function for the exponential distribution is discussed. The estimator obtained here has higher efficiency as compared to the one where the shrinkage factor is not subjected to bootstrapping.
Some Estimators For The Population Mean Using Auxiliary Information Under Ranked Set Sampling, Walid A. Abu-Dayyeh, M. S. Ahmed, R. A. Ahmed, Hassen A. Muttlak
Some Estimators For The Population Mean Using Auxiliary Information Under Ranked Set Sampling, Walid A. Abu-Dayyeh, M. S. Ahmed, R. A. Ahmed, Hassen A. Muttlak
Journal of Modern Applied Statistical Methods
Auxiliary information is used along with ranking information to derive several classes of estimators to estimate the population mean of a variable of interest based on RSS (ranked set sample). The properties of these newly suggested estimators were examined. Comparisons between special cases of these estimators and other known estimators are made using a real data set. Some of the new estimators are superior to the old ones in terms of bias and mean square error.
The Efficiency Of Ols In The Presence Of Auto-Correlated Disturbances In Regression Models, Samir Safi, Alexander White
The Efficiency Of Ols In The Presence Of Auto-Correlated Disturbances In Regression Models, Samir Safi, Alexander White
Journal of Modern Applied Statistical Methods
The ordinary least squares (OLS) estimates in the regression model are efficient when the disturbances have mean zero, constant variance, and are uncorrelated. In problems concerning time series, it is often the case that the disturbances are correlated. Using computer simulations, the robustness of various estimators are considered, including estimated generalized least squares. It was found that if the disturbance structure is autoregressive and the dependent variable is nonstochastic and linear or quadratic, the OLS performs nearly as well as its competitors. For other forms of the dependent variable, rules of thumb are presented to guide practitioners in the choice …
Interval Estimation Of Risk Difference In Simple Compliance Randomized Trials, Kung-Jong Lui
Interval Estimation Of Risk Difference In Simple Compliance Randomized Trials, Kung-Jong Lui
Journal of Modern Applied Statistical Methods
Consider the simple compliance randomized trial, in which patients randomly assigned to the experimental treatment may switch to receive the standard treatment, while patients randomly assigned to the standard treatment are all assumed to receive their assigned treatment. Six asymptotic interval estimators for the risk difference in probabilities of response among patients who would accept the experimental treatment were developed. Monte Carlo methods were employed to evaluate and compare the finite-sample performance of these estimators. An example studying the effect of vitamin A supplementation on reducing mortality in preschool children was included to illustrate their practical use.
Estimation Using Bivariate Extreme Ranked Set Sampling With Application To The Bivariate Normal Distribution, Mohammad Fraiwan Al-Saleh, Hani M. Samawi
Estimation Using Bivariate Extreme Ranked Set Sampling With Application To The Bivariate Normal Distribution, Mohammad Fraiwan Al-Saleh, Hani M. Samawi
Journal of Modern Applied Statistical Methods
In this article, the procedure of bivariate extreme ranked set sampling (BVERSS) is introduced and investigated as a procedure of obtaining more accurate samples for estimating the parameters of bivariate populations. This procedure takes its strength from the advantages of bivariate ranked set sampling (BVRSS) over the usual ranked set sampling in dealing with two characteristics simultaneously, and the advantages of extreme ranked set sampling (ERSS) over usual RSS in reducing the ranking errors and hence in being more applicable. The BVERSS procedure will be applied to the case of the parameters of the bivariate normal distributions. Illustration using real …