On comparing,classifying and clustering several dependent regression models

Classifying several regression models fitted on a dataset is one of the most problems in data analysis. In other words, scientists are interested in comparing several regression models that can be used for a dataset. In this paper, an approach will be used to compare and classify several dependent regression models. Then the performance of the proposed method is investigated using simulation study and real example.     

Testing the equality of two independent regression models

In some situations, for example in agriculture, biology, hydrology, and psychology, researchers wish to determine whether the relationship between response variable and predictor variables differs in two populations. In other words, we are interested in comparing two regression models for two independent datasets. In this work, we will use the parametric and nonparametric methods to establish hypothesis testing for the equality of two independent regression models. Then the simulation study is provided to investigate the performance of the proposed method.     

A Desire to Return to the Country of Birth as a Function of Language Preference: An Empirical Study with Iranian Immigrants in the United States

It was hypothesized that bilingual Iranian immigrants in the United States who prefer to use the Persian language, compared to Iranians who prefer to use English, would be more likely to express a desire to return to Iran to live. Participants were 292 bilingual Iranian immigrants in the United States (128 men, 164 women) who completed a questionnaire with a similar content, prepared in Persian on one side and in English on the other side. Participants were given the option to either complete the Persian or English version of the questionnaire. Results confirmed the research hypothesis suggesting that the immigrants’ preference in using their native language serves as a “pull” factor that increases the probability of a desire to return to the country of birth. It was also found that women and younger respondents were less likely to express a desire to return to Iran. Level of education, marital status, years of living in the United States, and attitudes toward marriage and the family did not predict such a desire. Implications for acculturation research and for counseling culturally diverse clients are discussed. Policy implications to attract immigrants back to their country of birth were also discussed.     

Testing the difference between spectral densities of two independent periodically correlated (cyclostationary) time series models

In this work, the asymptotic distribution for the discrete Fourier transform of periodically correlated (PC) processes is applied to test the equality of two PC time series. Then the performance of the proposed method is investigated through the Monte Carlo simulations.     

Testing the difference between two independent regression models

ABSTRACT

In some situations, for example, in biology or psychology studies, we wish to determine whether the linear relationship between response variable and predictor variables differs in two populations. The analysis of the covariance (ANCOVA) or, equivalently, the partial F-test approaches are the commonly used methods. In this study, the asymptotic distribution for the difference between two independent regression coefficients was established. The proposed method was used to derive the asymptotic confidence set for the difference between coefficients and hypothesis testing for the equality of the two regression models. Then a simulation study was conducted to compare the proposed method with the partial F method. The performance of the new method was comparable with that of the partial F method.     

On the ratio of two independent skewnesses

In many fields, the researchers are interested in making inferences about the ratio of skewnesses in two independent populations. In the present paper, the asymptotic distribution for the ratio of the sample skewnesses in two independent populations is established. Then the asymptotic distribution is used to derive the asymptotic confidence interval and to test the hypothesis for the ratio of population's skewnesses. Finally, the applicability of the proposed method is investigated through Monte Carlo simulations.     

Admissible and Minimax Estimators of θ r with Truncated Parameter Space Under Squared-Log Error Loss Function

Admissible and minimax estimation of θ ^r in a subclass of the exponential family with truncated parameter space is considered under squared-log error loss function. We give a necessary and sufficient condition for minimaxity and obtain the classes of new admissible and minimax estimators.     

Two-Piece location-scale distributions based on scale mixtures of normal family

In this work, we study the maximum likelihood (ML) estimation problem for the parameters of the two-piece (TP) distribution based on the scale mixtures of normal (SMN) distributions. This is a family of skewed distributions that also includes the scales mixtures of normal class, and is flexible enough for modeling symmetric and asymmetric data. The ML estimates of the proposed model parameters are obtained via an expectation-maximization (EM)-type algorithm.     

The compound class of linear failure rate-power series distributions: Model,properties, and applications

In this article, a new class of distributions is introduced, which generalizes the linear failure rate distribution and is obtained by compounding this distribution and power series class of distributions. This new class of distributions is called the linear failure rate-power series distributions and contains some new distributions such as linear failure rate-geometric, linear failure rate-Poisson, linear failure rate-logarithmic, linear failure rate-binomial distributions, and Rayleigh-power series class of distributions. Some former works such as exponential-power series class of distributions, exponential-geometric, exponential-Poisson, and exponential-logarithmic distributions are special cases of the new proposed model. The ability of the linear failure rate-power series class of distributions is in covering five possible hazard rate function, that is, increasing, decreasing, upside-down bathtub (unimodal), bathtub and increasing-decreasing-increasing shaped. Several properties of this class of distributions such as moments, maximum likelihood estimation procedure via an EM-algorithm and inference for a large sample, are discussed in this article. In order to show the flexibility and potentiality, the fitted results of the new class of distributions and some of its submodels are compared using two real datasets.