部分线性模型的光滑样条推断

运用光滑样条估计部分线性模型中的非参数函数,利用限制最大似然或广义交叉验证(GCV)的方法选择光滑参数,主要考察了部分线性模型的光滑样条估计以及有关非参数函数部分的假设检验.基于光滑参数的选择方法,提出了部分线性模型中的非参数函数是否为多项式函数的假设检验方法,并通过模拟例子研究本文提出的推断效果.     

序线性拓扑空间中多目标规划的最优性条件

利用序线性拓扑空间中的次似凸映射下的择一定理得到多目标规划的最优性条件。     

旅游产品传播中构建消费文化空间

旅游产品对文化和传播具有很强的依赖性,本文分析了旅游与文化产业的关系、旅游产品传播体系的现状及旅游产品传播体系中出现的问题,进而提出在旅游产品传播中构建消费文化空间的建议。     

拓扑空间中的KKM型定理的推广及其应用

在FC-空间的非紧子集上引入了R-SKKM映射,建立了关于紧闭值和转移紧闭值的R-SKKM映射的R-SKKM定理,得到了FC-空间中的极大极小不等式定理和鞍点定理.     

Banach空间中一类广义混合平衡问题系统解的存在性及算法

在实Banach空间中引入和研究了一类新的广义混合平衡问题组.首先考虑了一个辅助混合平衡问题组,通过它与广义混合平衡问题组的等价性,证明了它的解的存在性和唯一性;其次构造了广义混合平衡问题组的近似解的迭代算法.在一定的条件下,证明了由算法生成的迭代序列的强收敛性,这些结果推广并改进了近期的某些结果.     

广义bent函数的性质与构造

广义bent函数f（x）对应的每个Walsh谱取值均相等,此时f（x）与仿射函数g（x）=x×y＋b（y∈ii,b∈Zq）的距离可证明都相等,这使得广义bent函数的非线性度达到最大。这种函数在保密和通信中有许多重要的应用。本文首先讨论了广义bent函数的一些性质,且通过这些性质在已有结论的基础上给出构造广义bent函数的一些方法,并在之后给出了证明。     

Elaborating pedagogical content knowledge: what it means to know students and think about teaching

The purpose of this study was to examine how a teacher understood her students and then thought and made decisions about content, curriculum and pedagogy. Pedagogical content knowledge (PCK) and Deweyan philosophies of experience and education provided the theoretical frameworks. Data were collected through observations (N?=?38) and interviews (N?=?38) over four months and analysed using constant comparison. Findings indicated that this teacher possessed a broad repertoire of knowledge about students that she used to think and make decisions about content, curriculum and pedagogy. The connections between knowing students and thinking about teaching were more sophisticated and interconnected than is typically recognized or articulated in teacher knowledge literature. Three themes are used to explain how this teacher understood her students’ emotional and social lives in and out of her classroom, and ways it influenced her thinking and teaching. The discussion centers on the need for more comprehensive analyses of teachers’ pedagogical content knowledge.     

Poisson Growth Mixture Modeling of Intensive Longitudinal Data: An Application to Smoking Cessation Behavior

This article illustrates the relation between structural equation modeling (SEM) and canonical correlation analysis (CCA). The representation of CCA in SEM may provide some important interpretive information that is not available from conventional CCA, that is, statistical tests for the canonical function and index coefficients, and statistical tests for individual canonical functions. Hierarchically, the relation between the two analytic approaches suggests that SEM stands to be a more general analytic approach. For researchers interested in these techniques, an understanding of the interrelation among them can be helpful to our choice of analytic method.     

The Partial Credit Model and Generalized Partial Credit Model as Constrained Nominal Response Models,With Applications in Mplus

The purpose of this article is to demonstrate constraining the nominal response model in Mplus software to calibrate data under the partial credit model (PCM) and generalized partial credit model (GPCM). Currently, many researchers are uncertain if the PCM and GPCM can be estimated within Mplus. Through model constraint commands in Mplus, we demonstrate that both models can be estimated in recent versions of this software. We present an example of this approach with data from 522 respondents on a subset of items from the Math Self-Efficacy Scale (Betz & Hackett, 1983). It is demonstrated that the presented model code is a viable way of estimating the models in Mplus.     

Modeling Nonlinear Structural Equation Models: A Comparison of the Two-Stage Generalized Additive Models and the Finite Mixture Structural Equation Model

Researchers have devoted some time and effort to developing methods for fitting nonlinear relationships among latent variables. In particular, most of these have focused on correctly modeling interactions between 2 exogenous latent variables, and quadratic relationships between exogenous and endogenous variables. All of these approaches require prespecification of the nonlinearity by the researcher, and are limited to fairly simple nonlinear relationships. Other work has been done using mixture structural equation models (SEMM) in an attempt to fit more complex nonlinear relationships. This study expands on this earlier work by introducing the 2-stage generalized additive model (2SGAM) approach for fitting regression splines in the context of structural equation models. The model is first described and then investigated through the use of simulated data, in which it was compared with the SEMM approach. Results demonstrate that the 2SGAM is an effective tool for fitting a variety of nonlinear relationships between latent variables, and can be easily and accurately extended to models including multiple latent variables. Implications of these results are discussed.