Self-signaling models predict less selfish behavior in a probabilistic giving setting as individuals are expected to invest pro-social identity. However, there is also substantial evidence that people tend exploit situational excuses for choices (for instance, uncertainty) and behave more selfishly. We contrast these two motivations (identity management self-deception) experimentally order test...

The Western assumption that talking is connected to thinking is not shared in the East. The research examines how the actual psychology of individuals reflects these different cultural assumptions. In Study 1, Asian Americans and European Americans thought aloud while solving reasoning problems. Talking impaired Asian Americans' performance but not that of European Americans. Study 2 showed tha...

A two-level regression mixture model is discussed and contrasted with the conventional two-level regression model. Simulated and real data shed light on the modelling alternatives. The real data analyses investigate gender differences in mathematics achievement from the US National Education Longitudinal Survey.The two-level regression mixture analyses show that unobserved heterogeneity should ...

background: although the cox proportional hazard regression is the most popular model for analyzing the prognostic factors on survival of cancer patients, under certain circumstances, parametric models estimate the parameter more efficiently than the cox model.  the aim of this study was to compare the cox regression model  with parametric models in patients with gastric cancer who registered a...

 Traditional regression analyses assume normality of observations and independence of mean and variance. However, there are many examples in science and Technology where the observations come from a skewed distribution and moreover there is a functional dependence between variance and mean. In this article, we propose a method for regression analysis under Inverse Gaussian model when th...

Nonparametric regression analysis provides statistical tools for estimating regression curves or surfaces from noisy data. Conventional nonparametric regression procedures, however, are only appropriate for estimating continuous regression functions. When the underlying regression function has jumps, functions estimated by the conventional procedures are not statistically consistent at the jump...

We consider the problem of regression analysis with imprecise data. By imprecise data we mean imprecise observations of precise quantities in the form of sets of values. In this paper, we explore a recently introduced likelihood-based approach to regression with such data. The approach is very general, since it covers all kinds of imprecise data (i.e. not only intervals) and it is not restricte...