“It Is What It Is”: Masculinity,Homosexuality, and Inclusive Discourse in Mixed Martial Arts

In this article we make use of inclusive masculinity theory to explore online media representations of male homosexuality and masculinity within the increasingly popular combat sport of mixed martial arts (MMA). Adopting a case-study approach, we discuss narratives constructed around one aspirational male MMA fighter, Dakota Cochrane, whose history of having participated in gay pornography became a major talking point on a number of MMA discussion/community Web sites during early 2012. While these narratives attempted to discursively rescue Cochrane’s supposedly threatened masculinity, highlighting both his “true” heterosexuality and his prodigious fighting abilities, they also simultaneously celebrated the acceptance of homosexual men within the sport that Cochrane’s case implied. Thus, we suggest that these media representations of homosexuality and masculinity within MMA are indicative of declining cultural homophobia and homohysteria and an inclusive vision of masculinity, as previously described by proponents of inclusive masculinity theory.     

金融自由化浪潮下我国金融业发展对策

20世纪70年代以来,国际金融自由化的进程明显加快,金融工具不断创新,金融信息化蓬勃发展.利率市场化、放松金融管制、开放国内资本市场、加强对全球金融的统一监管成为大势所趋.文章通过国际金融自由化进程中一些规律性经验的总结及我国金融改革历程的回顾,对我国金融市场在利率、外汇及证券等业务的开放提出了建议.     

十六届三中全会后我国所有制问题最新研究综述

党的十六届三中全会提出,“要大力发展混合所有制经济,使股份制成为公有制的主要实现形式”,引发经济学界对所有制问题展开了激烈的争论。学界对混合所有制经济提出许多不同的理解,对股份制企业的所有制性质作出许多不同的判断。鉴于此,本文对十六届三中全会以来国内对所有制问题的争论作一个简要的综述,以反映我国所有制问题研究的最新进展。     

Performance of traditional f tests in repeated measures designs under covariance heterogeneity

This study investigates the performance of two traditional F tests, one for main effects and the other for interaction in repeated measures designs under several conditions of covariance heterogeneity. Overall, the test for interaction is more vulnerable than the one for main effects. Distortion in the level of significance is less serious for the case of equal group size.     

Regression for non-Euclidean data using distance matrices

Regression methods for common data types such as measured, count and categorical variables are well understood but increasingly statisticians need ways to model relationships between variable types such as shapes, curves, trees, correlation matrices and images that do not fit into the standard framework. Data types that lie in metric spaces but not in vector spaces are difficult to use within the usual regression setting, either as the response and/or a predictor. We represent the information in these variables using distance matrices which requires only the specification of a distance function. A low-dimensional representation of such distance matrices can be obtained using methods such as multidimensional scaling. Once these variables have been represented as scores, an internal model linking the predictors and the responses can be developed using standard methods. We call scoring as the transformation from a new observation to a score, whereas backscoring is a method to represent a score as an observation in the data space. Both methods are essential for prediction and explanation. We illustrate the methodology for shape data, unregistered curve data and correlation matrices using motion capture data from an experiment to study the motion of children with cleft lip.     

Confounding in mixed factorial experiments through isomorphisms

The derivation of a simpie mexhoa for confounding in mixed factorial experiments from an isomorphism of finite abelian groups is presented. The theoretical bases of confounding procedures that use modular arithmetic for such experiments are compared.     

论量刑程序改革

量刑程序改革是对我国现行法律规定和相关理论的贯彻落实,其目标是为了纠正片面的司法观念,解决量刑的失衡问题,实现量刑的公平正义,加强对公民权利的保障。量刑程序改革的价值应包括公正、公开和效率。在优化资源配置的基础上,我国可以采用混合型的定罪量刑关系模式。     

Generalized additive mixed models

Following the extension from linear mixed models to additive mixed models, extension from generalized linear mixed models to generalized additive mixed models is made, Algorithms are developed to compute the MLE's of the nonlinear effects and the covariance structures based on the penalized marginal likelihood. Convergence of the algorithms and selection of the smooth param¬eters are discussed.     

Diagnostics for elliptical linear mixed models with first-order autoregressive errors

For longitudinal time series data, linear mixed models that contain both random effects across individuals and first-order autoregressive errors within individuals may be appropriate. Some statistical diagnostics based on the models under a proposed elliptical error structure are developed in this work. It is well known that the class of elliptical distributions offers a more flexible framework for modelling since it contains both light- and heavy-tailed distributions. Iterative procedures for the maximum-likelihood estimates of the model parameters are presented. Score tests for the presence of autocorrelation and the homogeneity of autocorrelation coefficients among individuals are constructed. The properties of test statistics are investigated through Monte Carlo simulations. The local influence method for the models is also given. The analysed results of a real data set illustrate the values of the models and diagnostic statistics.     

Several nonparametric and semiparametric approaches to linear mixed model regression

Mixed models are powerful tools for the analysis of clustered data and many extensions of the classical linear mixed model with normally distributed response have been established. As with all parametric (P) models, correctness of the assumed model is critical for the validity of the ensuing inference. An incorrectly specified P means model may be improved by using a local, or nonparametric (NP), model. Two local models are proposed by a pointwise weighting of the marginal and conditional variance–covariance matrices. However, NP models tend to fit to irregularities in the data and may provide fits with high variance. Model robust regression techniques estimate mean response as a convex combination of a P and a NP model fit to the data. It is a semiparametric method by which incomplete or incorrectly specified P models can be improved by adding an appropriate amount of the NP fit. We compare the approximate integrated mean square error of the P, NP, and mixed model robust methods via a simulation study and apply these methods to two real data sets: the monthly wind speed data from countries in Ireland and the engine speed data.