Cem Kadilar

Ratio estimators in simple random sampling

This study proposes ratio estimators by adapting the estimators type of Ray and Singh [J. Ind. Stat. Assoc. 19 (1981) 147] to traditional and the other ratio-type estimators in simple random sampling in literature. Theoretically, mean square error (MSE) equations of all proposed ratio estimators are obtained and compared with each other. By these comparisons the conditions, which make each proposed estimator more efficient than the others, are found. The theoretical results are supported by a numerical illustration.

Forecasting nonlinear time series with a hybrid methodology

In recent years, artificial neural networks (ANNs) have been used for forecasting in time series in the literature. Although it is possible to model both linear and nonlinear structures in time series by using ANNs, they are not able to handle both structures equally well. Therefore, the hybrid methodology combining ARIMA and ANN models have been used in the literature. In this study, a new hybrid approach combining Elmans Recurrent Neural Networks (ERNN) and ARIMA models is proposed. The proposed hybrid approach is applied to Canadian Lynx data and it is found that the proposed approach has the best forecasting accuracy.

Improvement in estimating the population mean in simple random sampling

This paper proposes some estimators for the population mean using the ratio estimators presented in [C. Kadilar, H. Cingi, Ratio estimators in simple random sampling, Applied Mathematics and Computation 151 (2004) 893–902] and shows that all proposed estimators are always more efficient than the ratio estimators. This result is also supported by a numerical example.

Rapid analysis of sugars in honey by processing Raman spectrum using chemometric methods and artificial neural networks

The aim of this study was to quantify glucose, fructose, sucrose and maltose contents of honey samples using Raman spectroscopy as a rapid method. By performing a single measurement, quantifications of sugar contents have been said to be unaffordable according to the molecular similarities between sugar molecules in honey matrix. This bottleneck was overcome by coupling Raman spectroscopy with chemometric methods (principal component analysis (PCA) and partial least squares (PLS)) and an artificial neural network (ANN). Model solutions of four sugars were processed with PCA and significant separation was observed. This operation, done with the spectral features by using PLS and ANN methods, led to the discriminant analysis of sugar contents. Models/trained networks were created using a calibration data set and evaluated using a validation data set. The correlation coefficient values between actual and predicted values of glucose, fructose, sucrose and maltose were determined as 0.964, 0.965, 0.968 and 0.949 for PLS and 0.965, 0.965, 0.978 and 0.956 for ANN, respectively. The requirement of rapid analysis of sugar contents of commercial honeys has been met by the data processed within this article.

A new estimator using two auxiliary variables

Utilizing the estimator in Abu-Dayyeh et al. [Appl. Math. Comput. 139 (2003) 287], we suggest an estimator using two auxiliary variables in simple random sampling. We obtain mean square error (MSE) equation of the proposed estimator and theoretically compare it with the MSE of the traditional estimator using two auxiliary variables. By this comparison, we show the condition that the proposed estimator is more efficient than the traditional one. In addition, we support this theoretical result by an application with original data.

Ratio estimators for the population variance in simple and stratified random sampling

We propose some ratio-type variance estimators using ratio estimators for the population mean in literature. We obtain mean square error (MSE) equations of proposed estimators and show that proposed estimators are more efficient than the traditional ratio estimator, suggested by [C.T. Isaki, Variance estimation using auxiliary information, Journal of the American Statistical Association 78 (1983) 117-123], under certain conditions. We also adapt the proposed estimators in the simple random sampling to the stratified random sampling. In addition, we support the theoretical results with the aid of numerical examples.

A New Ratio Estimator in Stratified Random Sampling

Abstract In this article, we suggest a new ratio estimator in stratified random sampling based on the Prasad (1989) estimator. Theoretically, we obtain the mean square error (MSE) for this estimator and compare it with the MSE of traditional combined ratio estimate. By this comparison, we demonstrate that proposed estimator is more efficient than combined ratio estimate in all conditions. In addition, this theoretical result is supported by a numerical example.

On improvement in estimating population mean in stratified random sampling

Gupta and Shabbir 2 have suggested an alternative form of ratio-type estimators for estimating the population mean. In this paper, we obtained a corrected version for the mean square error (MSE) of the Gupta–Shabbir estimator, up to first order of approximation, and the optimum case is discussed. We expand this estimator to the stratified random sampling and propose general classes for combined and separate estimators. Also an empirical study is carried out to show the properties of the proposed estimators.

Family of Estimators of Population Mean Using Two Auxiliary Variables in Stratified Random Sampling

A general family of estimators, which use the information of two auxiliary variables in the stratified random sampling, is proposed to estimate the population mean of the variable under study. Under stratified random sampling without replacement scheme, the expressions of bias and mean square error (MSE) up to the first- and second-order approximations are derived. The family of estimators in its optimum case is discussed. Also, an empirical study is carried out to show the properties of the proposed estimators.

Management of Recurrent Malignant Pleural Effusion with Chemical Pleurodesis

PurposeMalignant pleural effusion is a common complication of primary and metastatic pleural malignancies. It is usually managed by drainage and pleurodesis, but there is no consensus as to the best method of pleurodesis. We compared the effectiveness, side effects, and cost of different chemical pleurodesis agents used in patients with malignant pleural effusion.MethodsBetween January 1990 and December 2001, 108 patients with malignant pleural effusion underwent chemical pleurodesis in our department. Thoracoscopy was performed in 64 patients (59%), a minithoracotomy in 18 (17%), tube thoracostomy in 11 (10%), and a small-bore catheter was inserted in 15 (14%). Talc was used in 68 (63%) patients, tetracycline in 26 (24%), and bleomycin in 14 (13%). Talc was instilled by insufflation during surgery after drainage, whereas tetracycline and bleomycin were instilled via tube or catheter for pleural analgesia.ResultsTalc resulted in significantly earlier tube and catheter removal, after an average 4.1 days versus 5.1 days after tetracyline, and 6.3 days after bleomycin (P = 0.026, P = 0.001, respectively). A significantly lower reaccumulation ratio in 90 days was achieved by the talc group, with nine (13.2%) patients, representing an 86.8% success rate, than in the tetracyline and bleomycin groups, with seven (26.7%) and five (35.7%) patients, respectively, representing 73.8% and 64.3% success rates (P = 0.04).ConclusionsTalc resulted in the earliest expansion, minimal drainage, and the earliest tube and catheter removal.