家庭早期读写训练与儿童读写成绩间的关系研究

利用儿童家庭一学校早期语言和识字能力系列测验对每个目标儿童语言与识字能力发展进行了评定，并着重揭示了这些测量结果与儿童家庭语言、识字测量指标之间关系。结果表明：(1)家庭早期启发性谈话可以预测儿童完成定义任务和对熊故事进行建构的程度；(2)吃饭期间说明性和解释性谈话可预测儿童词汇量的发展状况和理解力；(3)书本阅读可预测儿童识字应变能力及对故事的理解和建构能力；(4)儿童早期玩具游戏与后来读写成绩之间无关。     

Sufficient dimension reduction through informative predictor subspace

The purpose of this paper is to define the central informative predictor subspace to contain the central subspace and to develop methods for estimating the former subspace. Potential advantages of the proposed methods are no requirements of linearity, constant variance and coverage conditions in methodological developments. Therefore, the central informative predictor subspace gives us the benefit of restoring the central subspace exhaustively despite failing the conditions. Numerical studies confirm the theories, and real data analyses are presented.     

Improvement of mixed predictors in linear mixed models

In this paper, we introduce stochastic-restricted Liu predictors which will be defined by combining in a special way the two approaches followed in obtaining the mixed predictors and the Liu predictors in the linear mixed models. Superiorities of the linear combination of the new predictor to the Liu and mixed predictors are done in the sense of mean square error matrix criterion. Finally, numerical examples and a simulation study are done to illustrate the findings. In numerical examples, we took some arbitrary observations from the data as the prior information since we did not have historical data or additional information about the data sets. The results show that this case does the new estimator gain efficiency over the constituent estimators and provide accurate estimation and prediction of the data.     

Prediction in multilevel generalized linear models

Summary.  We discuss prediction of random effects and of expected responses in multilevel generalized linear models. Prediction of random effects is useful for instance in small area estimation and disease mapping, effectiveness studies and model diagnostics. Prediction of expected responses is useful for planning, model interpretation and diagnostics. For prediction of random effects, we concentrate on empirical Bayes prediction and discuss three different kinds of standard errors; the posterior standard deviation and the marginal prediction error standard deviation (comparative standard errors) and the marginal sampling standard deviation (diagnostic standard error). Analytical expressions are available only for linear models and are provided in an appendix . For other multilevel generalized linear models we present approximations and suggest using parametric bootstrapping to obtain standard errors. We also discuss prediction of expectations of responses or probabilities for a new unit in a hypothetical cluster, or in a new (randomly sampled) cluster or in an existing cluster. The methods are implemented in gllamm and illustrated by applying them to survey data on reading proficiency of children nested in schools. Simulations are used to assess the performance of various predictions and associated standard errors for logistic random-intercept models under a range of conditions.     

Small area estimation under unit-level temporal linear mixed models

Data from past time periods and temporal correlation are rich sources of information for estimating small area parameters at the current period. This paper investigates the use of unit-level temporal linear mixed models for estimating linear parameters. Two models are considered, with domain and domain-time random effects. The first model assumes time independency and the second one AR(1)-type time correlation. They are fitted by a Fisher-scoring algorithm that calculates the residual maximum likelihood estimators of the model parameters. Based on the introduced models, empirical best linear unbiased predictors of small area linear parameters are studied, and analytic estimators for evaluating the performance of their mean squared errors are proposed. Three simulation experiments are carried out to study the behaviour of the fitting algorithm, the small area predictors and the estimators of the mean squared error. By using data of the Spanish surveys of income and living conditions of 2004–2008, an application to the estimation of 2008 average normalized net annual incomes in Spanish provinces by sex is given.     

A COMPARISON OF ANALYSIS METHODS FOR LATE‐STAGE VARIETY EVALUATION TRIALS

The statistical analysis of late‐stage variety evaluation trials using a mixed model is described, with one‐ or two‐stage approaches to the analysis. Two sets of trials, from Australia and the UK, were used to provide realistic scenarios for a simulation study to evaluate the different methods of analysis. This study showed that a one‐stage approach gave the most accurate predictions of variety performance overall or within each environment, across a range of models, as measured by mean squared error of prediction or realized genetic gain. A weighted two‐stage approach performed adequately for variety predictions both overall and within environments, but a two‐stage unweighted approach performed poorly in both cases. A generalized heritability measure was developed to compare methods.     

Predicting Consumption Poverty using Non-Consumption Indicators: Experiments using Indonesian Data

Household consumption expenditure data is crucial for calculating important welfare measures such as poverty headcount rate. However, collecting such data is difficult and cumbersome. As an alternative, we experiment with three methods – consumption correlates model, poverty probability model, and wealth index principal components analysis (PCA) – to predict consumption expenditure and poverty using non-consumption indicators. The purpose is to use these alternatives for rapid monitoring and appraisal of social welfare as an early warning system. We test each method’s performance and find that the consumption correlates model is the best method to predict poverty quickly and relatively accurately. We find that education level, asset ownership, and consumption pattern are the best predictors of expenditure and poverty.     

Modeling proportions and marginal counts simultaneously for clustered multinomial data with random cluster sizes

Clustered multinomial data with random cluster sizes commonly appear in health, environmental and ecological studies. Traditional approaches for analyzing clustered multinomial data contemplate two assumptions. One of these assumptions is that cluster sizes are fixed, whereas the other demands cluster sizes to be positive. Randomness of the cluster sizes may be the determinant of the within-cluster correlation and between-cluster variation. We propose a baseline-category mixed model for clustered multinomial data with random cluster sizes based on Poisson mixed models. Our orthodox best linear unbiased predictor approach to this model depends only on the moment structure of unobserved distribution-free random effects. Our approach also consolidates the marginal and conditional modeling interpretations. Unlike the traditional methods, our approach can accommodate both random and zero cluster sizes. Two real-life multinomial data examples, crime data and food contamination data, are used to manifest our proposed methodology.