On shifting ground: First-time parents’ ideal world of paid work and family time

We examined first-time fathers’ and mothers’ perspectives about their ideal world of support in the context of dominant social ideology, ethno-cultural ideals, and the pragmatics of their everyday family, workplace, and socioeconomic circumstances during their first 18 months as parents. Twelve Canadian-born and six Chinese immigrant couples participated in individual in-depth interviews. We conducted a three-part analysis: fathers’ perspectives, mothers’ perspectives, and couples’ perspectives. Fathers focused on fulfilling dual fathering ideals of ‘time with family’ and ‘providing for family’; mothers emphasized fulfilling a mothering ideal of ‘caring for children'. Examining couples’ perspectives revealed a more nuanced understanding of their shared focus on ‘caring for family'. Parents in this study found the current social ideal of the ‘new’ father, who is both financial provider and nurturing co-parent, appealing, yet difficult to achieve. Couples wanted informal (i.e. family and social network) support, along with formal (i.e. workplace and childcare) support to enable them to provide family care and financial stability for their family. Findings contribute to understanding family and paid work experiences and decision-making among couples as new parents. We offer insights into the complexity of intersections among social ideals, personal expectations, family care, and paid work for fathers and mothers.     

Sexual Coercion in Marriage: Narrative Accounts of Abused Mexican-American Women

Intimate partner sexual violence (IPSV) has been relatively understudied particularly from the perspective of women of color. This study documents the experiences with partner sexual violence of ten Mexican-American women. Qualitative interviews were conducted using snowball sampling with women that had been in abusive marital relationships. A phenomenological approach was used and through a thematic analysis, participant narrative responses were synthesized into themes. All participants reported sexual coercion and violence in their marriages, yet most did not label those experiences as “rape” despite the threatening context in which these incidents occurred. Tacit, but not always explicit acceptance of machismo, patriarchy, and male dominance were common themes possibly increasing IPV risk. Results highlight the complex interaction between upholding cultural values on the one hand, and preserving individual well-being and safety, on the other, underscoring the need for more extensive culturally sensitive research, prevention and intervention efforts for abused Mexican-American women     

Joint GEEs for multivariate correlated data with incomplete binary outcomes

This study considers a fully-parametric but uncongenial multiple imputation (MI) inference to jointly analyze incomplete binary response variables observed in a correlated data settings. Multiple imputation model is specified as a fully-parametric model based on a multivariate extension of mixed-effects models. Dichotomized imputed datasets are then analyzed using joint GEE models where covariates are associated with the marginal mean of responses with response-specific regression coefficients and a Kronecker product is accommodated for cluster-specific correlation structure for a given response variable and correlation structure between multiple response variables. The validity of the proposed MI-based JGEE (MI-JGEE) approach is assessed through a Monte Carlo simulation study under different scenarios. The simulation results, which are evaluated in terms of bias, mean-squared error, and coverage rate, show that MI-JGEE has promising inferential properties even when the underlying multiple imputation is misspecified. Finally, Adolescent Alcohol Prevention Trial data are used for illustration.     

Marginal models for the association structure of hierarchical binary responses

Clustered binary responses are often found in ecological studies. Data analysis may include modeling the marginal probability response. However, when the association is the main scientific focus, modeling the correlation structure between pairs of responses is the key part of the analysis. Second-order generalized estimating equations (GEE) are established in the literature. Some of them are more efficient in computational terms, especially facing large clusters. Alternating logistic regression (ALR) and orthogonalized residual (ORTH) GEE methods are presented and compared in this paper. Simulation results show a slightly superiority of ALR over ORTH. Marginal probabilities and odds ratios are also estimated and compared in a real ecological study involving a three-level hierarchical clustering. ALR and ORTH models are useful for modeling complex association structure with large cluster sizes.     

Tests for Structural Changes in Time Series of Counts

We propose methods for detecting structural changes in time series with discrete‐valued observations. The detector statistics come in familiar L2‐type formulations incorporating the empirical probability generating function. Special emphasis is given to the popular models of integer autoregression and Poisson autoregression. For both models, we study mainly structural changes due to a change in distribution, but we also comment for the classical problem of parameter change. The asymptotic properties of the proposed test statistics are studied under the null hypothesis as well as under alternatives. A Monte Carlo power study on bootstrap versions of the new methods is also included along with a real data example.     

A general long-term aging model with different underlying activation mechanisms: Modeling,Bayesian estimation,and case influence diagnostics

In this paper we propose a general cure rate aging model. Our approach enables different underlying activation mechanisms which lead to the event of interest. The number of competing causes of the event of interest is assumed to follow a logarithmic distribution. The model is parameterized in terms of the cured fraction which is then linked to covariates. We explore the use of Markov chain Monte Carlo methods to develop a Bayesian analysis for the proposed model. Moreover, some discussions on the model selection to compare the fitted models are given, as well as case deletion influence diagnostics are developed for the joint posterior distribution based on the ψ-divergence, which has several divergence measures as particular cases, such as the Kullback–Leibler (K-L), J-distance, L₁ norm, and χ²-square divergence measures. Simulation studies are performed and experimental results are illustrated based on a real malignant melanoma data.     

Slashed generalized exponential distribution

In this paper, we introduce an extension of the generalized exponential (GE) distribution, making it more robust against possible influential observations. The new model is defined as the quotient between a GE random variable and a beta-distributed random variable with one unknown parameter. The resulting distribution is a distribution with greater kurtosis than the GE distribution. Probability properties of the distribution such as moments and asymmetry and kurtosis are studied. Likewise, statistical properties are investigated using the method of moments and the maximum likelihood approach. Two real data analyses are reported illustrating better performance of the new model over the GE model.     

Slashed generalized Rayleigh distribution

We introduce a new family of distributions suitable for fitting positive data sets with high kurtosis which is called the Slashed Generalized Rayleigh Distribution. This distribution arises as the quotient of two independent random variables, one being a generalized Rayleigh distribution in the numerator and the other a power of the uniform distribution in the denominator. We present properties and carry out estimation of the model parameters by moment and maximum likelihood (ML) methods. Finally, we conduct a small simulation study to evaluate the performance of ML estimators and analyze real data sets to illustrate the usefulness of the new model.