论二外拓展教学与综合人文素质的培养

本文对二外课程教学与培养学生的综合人文素质相关问题加以论述。     

The Nature and Correlates of Sibling Influence in Two‐Parent African American Families

Guided by research and theory on sibling similarities and differences, this study explored the nature and correlates of 2 processes of sibling influence—social learning and sibling differentiation—during adolescence. Participants included 2 adolescent‐age siblings (M = 16.29 years for older siblings and M = 12.59 years for younger siblings, respectively) from 166 two‐parent African American families. Significant nonlinear associations between these two influence dynamics and some sibling relationship qualities were discovered. For sibling differentiation, but not social learning, these links were further moderated by gender composition of the sibling dyad. Additional analyses revealed that youths' reports of social learning were generally linked to smaller differences between siblings, whereas differentiation processes were linked to greater differences in siblings' individual characteristics.     

Some results on unilateral ARMA lattice processes

This paper presents several linked results on unilateral autoregressive moving average processes on a rectangular lattice. It is shown that axially symmetric two-dimensional quadrant processes must be separable. Exact forms for the inverse variance matrix are obtained in some cases, which allow exact Gaussian maximum likelihood estimation and simulation. It is shown that generating functions can be used for extrapolation. The herringbone simulation method is discussed.     

Stakeholder perspectives on integrated services for people who experience chronic homelessness

This article concerns a SAMHSA-funded service intervention, Pathways to Independence, that combined the use of SAMHSA’s SSI/SSDI Outreach, Access, and Recovery approach to facilitating disability entitlement claims with financial management classes, mental health assessment and referral, and access to employment and housing for people experiencing chronic homelessness. The purpose of this study was to explore perceptions of this form of service delivery and the benefits and challenges of the program model from the perspective of those who experienced it. Focus groups were conducted with a total of 17 program stakeholders, including program participants, program staff, collaborating case management staff of the parent agency, and a landlord and an employer, with questions to each regarding the program and its impact. Focus groups were analyzed through independent coding followed by discussion to reach consensus on key themes. Five themes were identified across the groups: (a) the importance of relationships, (b) staff specialization and provision of individualized services, (c) an orientation to outcomes, (d) comprehensiveness of services and facilitation of connections to community resources, and (e) systems challenges. The Pathways to Independence program provides a working model for integrated employment, entitlement assessment and application, housing, and behavioral health treatment for persons experiencing chronic homelessness.     

Estimating multiple-membership logit models with mixed effects: indirect inference versus data cloning

Multiple-membership logit models with random effects are models for clustered binary data, where each statistical unit can belong to more than one group. The likelihood function of these models is analytically intractable. We propose two different approaches for parameter estimation: indirect inference and data cloning (DC). The former is a non-likelihood-based method which uses an auxiliary model to select reasonable estimates. We propose an auxiliary model with the same dimension of parameter space as the target model, which is particularly convenient to reach good estimates very fast. The latter method computes maximum likelihood estimates through the posterior distribution of an adequate Bayesian model, fitted to cloned data. We implement a DC algorithm specifically for multiple-membership models. A Monte Carlo experiment compares the two methods on simulated data. For further comparison, we also report Bayesian posterior mean and Integrated Nested Laplace Approximation hybrid DC estimates. Simulations show a negligible loss of efficiency for the indirect inference estimator, compensated by a relevant computational gain. The approaches are then illustrated with two real examples on matched paired data.     

Autocovariance Estimation in Regression with a Discontinuous Signal and m‐Dependent Errors: A Difference‐Based Approach

We discuss a class of difference‐based estimators for the autocovariance in nonparametric regression when the signal is discontinuous and the errors form a stationary m‐dependent process. These estimators circumvent the particularly challenging task of pre‐estimating such an unknown regression function. We provide finite‐sample expressions of their mean squared errors for piecewise constant signals and Gaussian errors. Based on this, we derive biased‐optimized estimates that do not depend on the unknown autocovariance structure. Notably, for positively correlated errors, that part of the variance of our estimators that depend on the signal is minimal as well. Further, we provide sufficient conditions for ‐consistency; this result is extended to piecewise Hölder regression with non‐Gaussian errors. We combine our biased‐optimized autocovariance estimates with a projection‐based approach and derive covariance matrix estimates, a method that is of independent interest. An R package, several simulations and an application to biophysical measurements complement this paper.     

Monitoring two-stage processes with a profile at the second stage

In the multistage processes, quality of a process or a product at each stage is related to the previous stage(s). This property is referred to as a cascade property. Sometimes, quality of a process is characterized by a profile. In this paper, we consider a two-stage process with a normal quality characteristic in the first stage and a simple linear regression profile in the second stage. Then we propose two methods to monitor quality characteristics in both stages. The performance of the proposed two methods is evaluated through a numerical example in terms of average run length criterion.