城市地铁变形预测的三次指数平滑法

详细介绍了指数平滑法及三次指数平滑法的预测过程,应用此方法对实测资料进行了预计和比较.结果表明,其平均相对误差都小于1%.由此可见,该方法对城市地铁变形预测具有一定的应用价值.同时,文中指出了应用指数平滑法的一些优点及应注意的一些问题.     

基于三次指数平滑法的黄山风景区旅游营业总收入预测分析

指数平滑法就是将历史统计数据消除统计数据的起伏波动状况,使其规则化,以便把握事物发展的方向和趋势的方法,根据近几年黄山风景区旅游营业总收入数据分析,利用三次指数平滑法预测2010年和2011年黄山风景区旅游营业总收入将达到12.3906亿元和13.2809亿元.     

Using Fit Indexes to Select a Covariance Model for Longitudinal Data

This study investigated the performance of fit indexes in selecting a covariance structure for longitudinal data. Data were simulated to follow a compound symmetry, first-order autoregressive, first-order moving average, or random-coefficients covariance structure. We examined the ability of the likelihood ratio test (LRT), root mean square error of approximation (RMSEA), comparative fit index (CFI), and Tucker–Lewis Index (TLI) to reject misspecified models with varying degrees of misspecification. With a sample size of 20, RMSEA, CFI, and TLI are high in both Type I and Type II error rates, whereas LRT has a high Type II error rate. With a sample size of 100, these indexes generally have satisfactory performance, but CFI and TLI are affected by a confounding effect of their baseline model. Akaike's Information Criterion (AIC) and Bayesian Information Criterion (BIC) have high success rates in identifying the true model when sample size is 100. A comparison with the mixed model approach indicates that separately modeling the means and covariance structures in structural equation modeling dramatically improves the success rate of AIC and BIC.     

On the Estimation of Nonlinear Mixed-Effects Models and Latent Curve Models for Longitudinal Data

Nonlinear models are effective tools for the analysis of longitudinal data. These models provide a flexible means for describing data that follow complex forms of change. Exponential and logistic functions that include a parameter to represent an asymptote, for instance, are useful for describing responses that tend to level off with time. There are forms of nonlinear latent curve models and nonlinear mixed-effects model that are equivalent, and so given the same set of data, growth function, distributional assumptions, and method of estimation, the 2 models yield equivalent results. There are also forms that are strikingly different and can yield different interpretations for a given set of data. This article discusses cases in which nonlinear mixed-effects models and nonlinear latent curve models are equivalent and those in which they are different and clarifies the estimation needs of the different models. Examples based on empirical data help to illustrate these points.     

基于数据融合技术的图像平滑方法

在分析边缘保持滤波器算法的基础上,应用多传感器数据融合技术,提出了一种新的图像平滑算法。该算法釆用线状掩模窗口和最优融合估计方法,有效地增强了边缘保持能力和去噪能力,克服了边缘保持滤波器存在的缺陷。实验结果表明,该算法性能优于边缘保持滤波器及经典均值滤波器。     

Investigating Stage-Sequential Growth Mixture Models with Multiphase Longitudinal Data

This article investigates three types of stage-sequential growth mixture models in the structural equation modeling framework for the analysis of multiple-phase longitudinal data. These models can be important tools for situations in which a single-phase growth mixture model produces distorted results and can allow researchers to better understand population heterogeneity and growth over multiple phases. Through theoretical and empirical comparisons of the models, the authors discuss strategies with respect to model selection and interpreting outcomes. The unique attributes of each approach are illustrated using ecological momentary assessment data from a tobacco cessation study. Transitional discrepancy between phases as well as growth factors are examined to see whether they can give us useful information related to a distal outcome, abstinence at 6 months postquit. It is argued that these statistical models are powerful and flexible tools for the analysis of complex and detailed longitudinal data.     

纵向数据半参数模型估计的相合性

考虑纵向数据半参数回归模型:Yij=XiTjβ+g(Tij)+iεj.基于最小二乘法和局部线性拟合的方法建立了模型中参数分量β,回归函数g(.)和误差方差σ2的估计,并在适当条件下证明了估计量的相合性.     

纵向数据常用参数分析方法比较

分析了纵向数据的本质特点,对纵向数据常用的参数分析方法的优劣进行了比较。     

方向平滑滤波的模板卷积实现

方向平滑滤波器兼有去噪和边缘保持的双重特性，在数字图像预处理中被经常采用。改变以往方向平滑滤波按定义的实现算法，而改用模板卷积，并采用邻近插值的插补模式。对比实验结果表明，此算法具有高效、省时特点。     

纵向数据半参数回归模型的渐近性质

考虑纵向数据半参数回归模型:Y=Xβ g(T) ε,采用二阶段估计方法给出了参数分量β和非参数分量g(t)的估计量^βN和^gN(t),并在适当条件下研究了这些估计量的渐近正态性.     

Growth Curve Modeling to Studying Change: A Comparison of Approaches Using Longitudinal Dyadic Data With Distinguishable Dyads

Although methodology articles have increasingly emphasized the need to analyze data from two members of a dyad simultaneously, the most popular method in substantive applications is to examine dyad members separately. This might be due to the underappreciation of the extra information simultaneous modeling strategies can provide. Therefore, the goal of this study was to compare multiple growth curve modeling approaches for longitudinal dyadic data (LDD) in both structural equation modeling and multilevel modeling frameworks. Models separately assessing change over time for distinguishable dyad members are compared to simultaneous models fitted to LDD from both dyad members. Furthermore, we compared the simultaneous default versus dependent approaches (whether dyad pairs’ Level 1 [or unique] residuals are allowed to covary and differ in variance). Results indicated that estimates of variance and covariance components led to conflicting results. We recommend the simultaneous dependent approach for inferring differences in change over time within a dyad.     

一个解非线性互补问题的非精确 Jacobian 光滑化方法

基于光滑互补函数,将非线性互补问题等价转化光滑方程组问题,构造了一个新的求解该光滑方程组的非精确 Jacobian 光滑化方法,该算法克服牛顿法解大规模互补问题的不便,并证明了该算法具有全局收敛性,在一定的假设条件下具有局部二次收敛性。     

一个求解非线性互补问题的光滑化全局收敛性算法

求解非线性互补问题的一种方法是将其转化为非光滑方程组。本文通过引进一个基于Fischer-Burmeister函数的光滑NCP函数[8],建立了求解P0函数非线性互补问题的一个新的光滑牛顿算法。这个算法在每步迭代中只需要解一个光滑方程且不要求给出具体光滑因子下降的过程。在一定的条件下,证明了该算法的全局收敛性。数值试验表明该算法是有效的.     

Developing and Validating a Novel Anonymous Method for Matching Longitudinal School-Based Data

Prospective longitudinal data collection is an important way for researchers and evaluators to assess change. In school-based settings, for low-risk and/or likely-beneficial interventions or surveys, data quality and ethical standards are both arguably stronger when using a waiver of parental consent—but doing so often requires the use of anonymous data collection methods. The standard solution to this problem has been the use of a self-generated identification code. However, such codes often incorporate personalized elements (e.g., birth month, middle initial) that, even when meeting the technical standard for anonymity, may raise concerns among both youth participants and their parents, potentially altering willingness to participate, response quality, or generating outrage. There may be value, therefore, in developing a self-generated identification code and matching approach that not only is technically anonymous but also appears anonymous to a research-naive individual. This article provides a proof of concept for a novel matching approach for school-based longitudinal data collection that potentially accomplishes this goal.     

The Actor–Partner Interdependence Model for Longitudinal Dyadic Data: An Implementation in the SEM Framework

In dyadic research, the actor–partner interdependence model (APIM) is widely used to model the effect of a predictor measured across dyad members on one’s own and one’s partner outcome. When such dyadic data are measured repeatedly over time, both the non-independence within couples and the non-independence over time need to be accounted for. In this paper, we present a longitudinal extension of the APIM, the L-APIM, that allows for both stable and time-varying sources of non-independence. Its implementation is readily available in multilevel software, such as proc mixed in SAS, but is lacking in the structural equation modeling (SEM) framework. We tackle the computational challenges associated with its SEM-implementation and propose a user-friendly free application for the L-APIM, which can be found at http://fgisteli.shinyapps.io/Shiny_LDD. As an illustration, we explore the actor and partner effects of positive relationship feelings on next day’s intimacy using 3-week diary data of 66 heterosexual couples.     

Digital Module 16: Longitudinal Data Analysis

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary.     

解P0非线性互补问题的非单调光滑牛顿法

基于CHKS光滑函数,将非线性互补问题转化为非线性光滑方程组,再构造光滑算子,将非线性光滑方程组转化为优化问题,且构造了一个新的牛顿算法,该算法引入了非单调线搜索,并在一定条件下证明了它的全局收敛性,及在非奇异条件而非严格互补条件条件下,证明了它的局部二次收敛性。最后给出数值实验结果。     

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.     

Smoothing Newton Algorithm for Nonlinear Complementarity Problem with a P.Function

By using a smoothing function,the P nonlinear complementarity problem(P NCP)can be reformulated as a parameterized smooth equation.A Newton method is proposed to solve this equation.The iteration sequence generated by the proposed algorithm is bounded and this algorithm is proved to be globally convergent under an assumption that the P NCP has a nonempty solution set.This assumption is weaker than the ones used in most existing smoothing algorithms.In particular,the solution obtained by the proposed algorithm is shown to be a maximally complementary solution of the P NCP without any additional assumption.     

Comparing Methods for Addressing Missingness in Longitudinal Modeling of Panel Data

Respondent attrition is a common problem in national longitudinal panel surveys. To make full use of the data, weights are provided to account for attrition. Weight adjustments are based on sampling design information and data from the base year; information from subsequent waves is typically not utilized. Alternative methods to address bias from nonresponse are full information maximum likelihood (FIML) or multiple imputation (MI). The effects on bias of growth parameter estimates from using these methods are compared via a simulation study. The results indicate that caution needs to be taken when utilizing panel weights when there is missing data, and to consider methods like FIML and MI, which are not as susceptible to the omission of important auxiliary variables.