Proceed With Caution? Parents' Union Dissolution and Children's Educational Achievement

Using high‐quality Norwegian register data on 49,879 children from 23,655 families, the authors estimated sibling fixed‐effects models to explore whether children who are younger at the time of a parental union dissolution perform less well academically, as measured by their grades at age 16, than their older siblings who have spent more time living with both biological parents. Results from a baseline model suggest a positive age gradient that is consistent with findings in some of the extant family structure literature. Once birth order is taken into account, the gradient reverses. When analyses also control for grade inflation by adding year of birth to the model, only those children who experience a dissolution just prior to receiving their grades appear relatively disadvantaged. The results illustrate the need to specify and interpret sibling fixed‐effects model with great care.     

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.     

Inferences on a life distribution by sampling from the ages or the ages at death

Consider a system where units having independent and identically distributed lifetimes enter according to a nonhomogeneous Poisson process. After the unit’s life in the system, the unit departs the system. For a fixed system time, this paper relates the units’ common underlying life distribution with the distribution of the ages of units in the system, the distribution for the system life of units that departed the system and the distribution for the system life of units that have recently departed the system. Results can be used to estimate the underlying life distribution or a truncated version of that distribution based on the ages and/or most recent ages at death in both one sample and two sample situations. Results include a complete characterization of the possible distribution of the ages of those units in the system, how to estimate the underlying life distribution from the most recent ages at death, and how to test for an underlying monotone failure rate function based on independent samples from the ages and most recent ages at death. Two sample inferences that involve a likelihood ratio ordering make use of the results in Dykstra et al. (1995, J Amer Statisc Assoc 90(431):1030–1040), which provides the maximum likelihood estimators and a likelihood ratio test when the two distributions satisfy a likelihood ratio ordering. For the ages of the active units and the ages at death among the departed units, limits for their distributions and strong limiting results for their empirical distributions will be provided.Disclaimer: The views expressed in this paper are those of the author and may not represent the views of the U.S. FDA.     

A semiparametric additive rates model for recurrent event data

Recurrent event data often arise in biomedical studies, with examples including hospitalizations, infections, and treatment failures. In observational studies, it is often of interest to estimate the effects of covariates on the marginal recurrent event rate. The majority of existing rate regression methods assume multiplicative covariate effects. We propose a semiparametric model for the marginal recurrent event rate, wherein the covariates are assumed to add to the unspecified baseline rate. Covariate effects are summarized by rate differences, meaning that the absolute effect on the rate function can be determined from the regression coefficient alone. We describe modifications of the proposed method to accommodate a terminating event (e.g., death). Proposed estimators of the regression parameters and baseline rate are shown to be consistent and asymptotically Gaussian. Simulation studies demonstrate that the asymptotic approximations are accurate in finite samples. The proposed methods are applied to a state-wide kidney transplant data set.     

“Being Raised by White People”: Navigating Racial Difference Among Adopted Multiracial Adults

There are increasing numbers of multiracial families created through marriage, adoption, birth, and a growing population of multiracial persons. Multiracials are a hidden but dominant group of transracially adopted children in both the United Kingdom and the United States. This paper introduces findings from an interpretive study of 25 transracially adopted multiracials regarding a set of experiences participants called “being raised by White people.” Three aspects of this experience are explored: (1) the centrality yet absence of racial resemblance, (2) navigating discordant parent‐child racial experiences, and (3) managing societal perceptions of transracial adoption. Whereas research suggests some parents believe race is less salient for multiracial children than for Black children, this study finds participants experienced highly racialized worlds into adulthood.     

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.