Duygu İçen

Fuzzy approach of group sequential test for binomial case

The aim of this study is to present the fuzzy statistics into group sequential test when response variable has binomial case. Confidence intervals for fuzzy parameter estimation in group sequential test procedure is applied to construct the related fuzzy test statistic with the help of Buckley’s approach with r-cuts. Afterwards, this present study is completed with a numerical application to real data. Finally it is concluded that the fuzzy approach is also applicable for group sequential tests when response variable has binomial case.

Error measures for fuzzy linear regression

HighlightsThe study covers different error measures that have not previously calculated for Monte Carlo study in fuzzy linear regression models.We obtain the most useful and the worst error measures to estimate fuzzy regression parameters without using any mathematical programming or heavy fuzzy arithmetic operations. The focus of this study is to use Monte Carlo method in fuzzy linear regression. The purpose of the study is to figure out the appropriate error measures for the estimation of fuzzy linear regression model parameters with Monte Carlo method. Since model parameters are estimated without any mathematical programming or heavy fuzzy arithmetic operations in fuzzy linear regression with Monte Carlo method. In the literature, only two error measures (E1 and E2) are available for the estimation of fuzzy linear regression model parameters. Additionally, accuracy of available error measures under the Monte Carlo procedure has not been evaluated. In this article, mean square error, mean percentage error, mean absolute percentage error, and symmetric mean absolute percentage error are proposed for the estimation of fuzzy linear regression model parameters with Monte Carlo method. Moreover, estimation accuracies of existing and proposed error measures are explored. Error measures are compared to each other in terms of estimation accuracy; hence, this study demonstrates that the best error measures to estimate fuzzy linear regression model parameters with Monte Carlo method are proved to be E1, E2, and the mean square error. One the other hand, the worst one can be given as the mean percentage error. These results would be useful to enrich the studies that have already focused on fuzzy linear regression models.

Different distance measures for fuzzy linear regression with Monte Carlo methods

The aim of this study was to determine the best distance measure for estimating the fuzzy linear regression model parameters with Monte Carlo (MC) methods. It is pointed out that only one distance measure is used for fuzzy linear regression with MC methods within the literature. Therefore, three different definitions of distance measure between two fuzzy numbers are introduced. Estimation accuracies of existing and proposed distance measures are explored with the simulation study. Distance measures are compared to each other in terms of estimation accuracy; hence this study demonstrates that the best distance measures to estimate fuzzy linear regression model parameters with MC methods are the distance measures defined by Kaufmann and Gupta (Introduction to fuzzy arithmetic theory and applications. Van Nostrand Reinhold, New York, 1991), Heilpern-2 (Fuzzy Sets Syst 91(2):259–268, 1997) and Chen and Hsieh (Aust J Intell Inf Process Syst 6(4):217–229, 2000). One the other hand, the worst distance measure is the distance measure used by Abdalla and Buckley (Soft Comput 11:991–996, 2007; Soft Comput 12:463–468, 2008). These results would be useful to enrich the studies that have already focused on fuzzy linear regression models.

Fuzzy probability calculation with confidence intervals in Bayesian networks

In this study, we propose to use Buckley’s confidence interval approach which has not been used before in the literature to calculate marginal and conditional fuzzy probabilities in Bayesian networks. We apply this approach to a real life problem and show that Buckley’s confidence interval approach provides to indicate uncertainty better and represents knowledge more explicitly than determining fuzzy probabilities based only on the expert opinion in Bayesian networks.

Hypothesis testing for the mean of inverse Gaussian distribution using α-cuts

In this study, we modify the method proposed by Buckley to testing statistical hypothesis for the mean of an inverse Gaussian distribution. In order to obtain fuzzy test statistic, we use confidence intervals by the help of

Differentially regulated ADAMTS1, 8, 9, and 18 in pancreas adenocarcinoma

\alpha

Can mastalgia be another somatic symptom in fibromyalgia syndrome

α-cuts. Then the method is applied to test the hypothesis for the mean of inverse Gaussian distribution when the scale parameter is known. Also a comparison is made between the fuzzy and non-fuzzy test procedure for the inverse Gaussian distribution.

Identification of some spanish olive cultivars using image processing techniques

BACKGROUND AND AIM Early diagnosis and histological subtyping are important issues in the management of patients with lung cancer (LC). The aim of this study is to investigate the diagnostic value of a panel of serum tumor markers in newly diagnosed patients with LC. METHODS Venous blood samples were collected from 99 patients with LC (42 adenocarcinoma, 35 squamous, and 22 small cell carcinoma) and 30 patients with benign lung disease. Progastrin releasing peptide (ProGRP), squamous cell carcinoma antigen (SCCAg), cytokeratin 19-fragments (CYFRA 21.1), human epididymis protein 4 (HE4), Chromogranin A (CgA) and neuron specific enolase (NSE) levels were measured. The diagnostic value of the biomarkers was assessed with ROC curve analyses; the area under the curve (AUC) was calculated. RESULTS Serum CYFRA 21.1, ProGRP, SCCAg, NSE levels were significantly higher in LC patients. While ProGRP levels were higher (p = 0.009) in SCLC; CYFRA 21.1 and SCCAg levels were higher in NSCLC (p = 0.019 and p = 0.001, respectively). The sensitivity and specificity of tumor markers were 72%, 83% for CYFRA 21.1; 70%, 57% for HE4; 18%, 93% for ProGRP; 43%, 77% for SCCAg; 54%, 53% for CgA; 73%, 50% for NSE. CYFRA 21.1 (p < 0.001, r = 0.394), HE4 (p = 0.014, r = 0.279) and CgA (p = 0.023, r = 0.259) levels were positively correlated with tumor stage in NSCLC. CgA levels were significantly higher in extensive stage SCLC (p = 0.004). CYFRA 21.1 had the highest diagnostic value for LC (AUC = 0.865). When it is combined with HE4, diagnostic value increased (AUC = 0.899). ProGRP had the highest diagnostic value (AUC = 0.875, p < 0.001) for discriminating SCLC from NSCLC. CONCLUSION A panel of three tumor markers CYFRA 21.1, HE4 and ProGRP may play a role for discriminating LC from benign lung disease and subtyping as SCLC.