Funding information Natural Sciences and Engineering Research Council (NSERC) of CanadaGamma regression is applied in several areas such as life testing, forecasting cancer incidences, genomics, rainfall prediction, experimental designs, and quality control. Gamma regression models allow for a monotone and no constant hazard in survival models. Owing to the broad applicability of gamma regression, we propose some novel and improved methods to estimate the coefficients of gamma regression model. We combine the unrestricted maximum likelihood (ML) estimators and the estimators that are restricted by linear hypothesis, and we present Stein-type shrinkage estimators (SEs). We then develop an asymptotic theory for SEs and obtain their asymptotic quadratic risks. In addition, we conduct Monte Carlo simulations to study the performance of the estimators in terms of their simulated relative efficiencies. It is evident from our studies that the proposed SEs outperform the usual ML estimators. Furthermore, some tabular and graphical representations are given as proofs of our assertions. This study is finally ended by appraising the performance of our estimators for a real prostate cancer data. KEYWORDS asymptotic quadratic risk, gamma regression, positive-part Stein-type shrinkage estimator, prostate cancer, relative efficiency, Stein-type shrinkage estimator 4310

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Obsessive-compulsive symptoms could be an important background for clinical disorder of OCD. The role of negative affect, rumination, and dispositional mindfulness has not been investigated in previous researches. Therefore, the purpose of this study was to study the relationship among negative affect, rumination, dispositional mindfulness with obsessive-compulsive symptoms. In a descriptive-correlational and crosses-sectional study, 283 students from University of Tabriz have selected by available sampling method during April through May 2017.Maudsley OCD inventory, Positive affect and negative affect scale (PANAS), Five Facet Mindfulness Questionnaire (FFMQ) and Rumination Scale of the Response Styles Questionnaire were used for collecting the data. Data were analyzed using Pearson correlation and multiple Regressions tests. The results showed that correlation between obsessive-compulsive symptoms and dispositional mindfulness was negative and significant, correlation between obsessive-compulsive symptoms and negative affect was positive and significant and correlation between obsessive-compulsive symptoms and rumination was significant. Also, negative affect, rumination, and dispositional mindfulness did able to predict the obsessivecompulsive symptoms. Dispositional mindfulness, negative affect, and rumination are the important determinants of obsessive-compulsive symptoms.