Adnan Karaibrahimoglu,Seren Ayhan,Mustafa Karaagac et al.
Adnan Karaibrahimoglu et al.
Dates have great importance in cancer diseases. However, the date variables themselves are not analyzed. This study aims to evaluate the descriptive statistics of diagnosis, operation, and last examination dates in gastric carcinoma patient...
A new alternative estimation method for Liu-type logistic estimator via particle swarm optimization: an application to data of collapse of Turkish commercial banks during the Asian financial crisis [0.03%]
基于粒子群优化的Liu型逻辑估计的新替代估计方法：应用于亚洲金融危机期间土耳其商业银行崩溃的数据
Nuriye Sancar,Deniz Inan
Nuriye Sancar
In the existence of multicollinearity problem in the logistic model, some important problems may occur in the analysis of the model, such as unstable maximum likelihood estimator with very high standard errors, false inferences. The Liu-typ...
Hao Chen,Minguang Zhang,Lanshan Han et al.
Hao Chen et al.
Marketing mix models (MMMs) are statistical models for measuring the effectiveness of various marketing activities such as promotion, media advertisement, etc. In this research, we propose a comprehensive marketing mix model that captures t...
Adrien Ehrhardt,Christophe Biernacki,Vincent Vandewalle et al.
Adrien Ehrhardt et al.
The granting process is based on the probability that the applicant will refund his/her loan given his/her characteristics. This probability, also called score, is learnt based on a dataset in which rejected applicants are excluded. Thus, t...
A new robust ridge parameter estimator based on search method for linear regression model [0.03%]
一种基于搜索方法的线性回归模型鲁棒岭参数估计器
Atila Göktaş,Özge Akkuş,Aykut Kuvat
Atila Göktaş
A large and wide variety of ridge parameter estimators proposed for linear regression models exist in the literature. Actually proposing new ridge parameter estimator lately proving its efficiency on few cases seems endless. However, so far...
Timothy E O&#x;Brien,Jack Silcox
Timothy E O&#x;Brien
The logit binomial logistic dose response model is commonly used in applied research to model binary outcomes as a function of the dose or concentration of a substance. This model is easily tailored to assess the relative potency of two sub...
Hatice Tul Kubra Akdur
Hatice Tul Kubra Akdur
Recently, unit-Lindley distribution and its associated regression models have been developed as an alternative to Beta regression model for which continuous outcome in the unit interval ( 0 , 1 ) . Proportion data usually occur in clinical ...
Fatma Başoğlu Kabran,Kamil Demirberk Ünlü
Fatma Başoğlu Kabran
In this paper, we are interested in predicting the bubbles in the S&P 500 stock market with a two-step machine learning approach that employs a real-time bubble detection test and support vector machine (SVM). SVM as a nonparametric binary ...
Huseyin Guler,Ebru Ozgur Guler
Huseyin Guler
Parameters of a linear regression model can be estimated with the help of traditional methods like generalized least squares and mixed estimator. However, recent developments increased the importance of big data sets, which have much more p...
Forecasting drought using neural network approaches with transformed time series data [0.03%]
基于变换时间序列数据的神经网络方法预测干旱事件
O Ozan Evkaya,Fatma Sevinç Kurnaz
O Ozan Evkaya
Drought is one of the important and costliest disaster all over the world. With the accelerated progress of climate change, its frequency of occurrence and negative impacts are rapidly increasing. It is crucial to initiate and sustain an ea...