Nonparametric Shrinkage Estimation for Aalen's Additive Hazards Model

Aalen's nonparametric additive model in which the regression coefficients are assumed to be unspecified functions of time is a flexible alternative to Cox's proportional hazards model when the proportionality assumption is in doubt. In this paper, we incorporate a general linear hypothesis into the estimation of the time‐varying regression coefficients. We combine unrestricted least squares estimators and estimators that are restricted by the linear hypothesis and produce James‐Stein‐type shrinkage estimators of the regression coefficients. We develop the asymptotic joint distribution of such restricted and unrestricted estimators and use this to study the relative performance of the proposed estimators via their integrated asymptotic distributional risks. We conduct Monte Carlo simulations to examine the relative performance of the estimators in terms of their integrated mean square errors. We also compare the performance of the proposed estimators with a recently devised LASSO estimator as well as with ridge‐type estimators both via simulations and data on the survival of primary billiary cirhosis patients.     

Collaboration and Internationalization of SMEs: Insights and Recommendations from a Systematic Review

This paper performs a systematic literature review of the undeniably diverse – and somewhat fragmented – current state of research on the collaborations and internationalization of small and medium‐sized enterprises (SMEs). We analyze key works and synthesize them into a framework that conceptually maps key antecedents, mediators, and moderators that influence the internationalization of SMEs. In addition, we highlight limitations of the literature, most notably in terms of theoretical fragmentation; extant theories are deployed and illustrated but rarely extended in a manner that significantly informs subsequent work. At an applied (but related) level, we argue the need for supplementary work that explores the distinct stages of internationalization – and the scope and scale of this process – rather than assuming closure around particular events. With this, we highlight the need for more rigorous and empirically informed explorations of contextual effects that take account of the consequences of developments in the global economic ecosystem.     

State transition modeling of complex monitored health data

This article considers the analysis of complex monitored health data, where often one or several signals are reflecting the current health status that can be represented by a finite number of states, in addition to a set of covariates. In particular, we consider a novel application of a non-parametric state intensity regression method in order to study time-dependent effects of covariates on the state transition intensities. The method can handle baseline, time varying as well as dynamic covariates. Because of the non-parametric nature, the method can handle different data types and challenges under minimal assumptions. If the signal that is reflecting the current health status is of continuous nature, we propose the application of a weighted median and a hysteresis filter as data pre-processing steps in order to facilitate robust analysis. In intensity regression, covariates can be aggregated by a suitable functional form over a time history window. We propose to study the estimated cumulative regression parameters for different choices of the time history window in order to investigate short- and long-term effects of the given covariates. The proposed framework is discussed and applied to resuscitation data of newborns collected in Tanzania.     

Assessing state substance abuse prevention infrastructure through the lens of CSAP's Strategic Prevention Framework

Although the organizational structures and operating procedures of state substance abuse prevention systems vary substantially across states, there is scant empirical research regarding approaches for rigorous assessment of system attributes and which attributes are most conducive to overall effectiveness. As one component of the national cross-site evaluation of the SPF State Incentive Grant Program (SPF SIG), an instrument was developed to assess state substance abuse prevention system infrastructure in order to measure infrastructure change and examine the role of state infrastructure in achieving prevention-related outcomes. In this paper we describe the development of this instrument and summarize findings from its baseline administration. As expected, states and territories were found to vary substantially with respect seven key characteristics, or domains, of state prevention infrastructure. Across the six domains that were assessed using numeric ratings, states scored highest on data systems and lowest on strategic planning. Positive intercorrelations were observed among these domains, indicating that states with high capacity on one domain generally have relatively high capacity on other domains as well. The findings also suggest that state prevention infrastructure development is linked to both funding from state government and the presence of a state interagency coordinating body with decision-making authority. The methodology and baseline findings presented will be used to inform the ongoing national cross-site evaluation of the SPF SIG and may provide useful information to guide further research on state substance abuse prevention infrastructure.     

Spatio-temporal patterns of pre-eclampsia and eclampsia in relation to drinking water salinity at the district level in Bangladesh from 2016 to 2018

Population and Environment - This analysis examines whether salinity in drinking water is associated with pre-eclampsia and eclampsia (PE/E), a leading cause of maternal morbidity and mortality....     

Migration,gender and low-paid work: on migrant men’s entry dynamics into the feminised social care work in the UK

The literature on workers in gender atypical occupations has been dominated by a focus on women doing men’s work. Much less attention has been paid to men in women’s work, and even less to the impact of migration. Based on 28 in-depth interviews with migrant men having experiences of working in hands-on social care in England, this article is a contribution to the understanding of migrant men’s entry dynamics into a female-dominated occupation. Focusing on migrant life experiences, it discusses how they actively engage in three entry dynamics: (1) facing barriers and negotiating them, (2) ‘stumbling upon’ women’s work, then developing compensating strategies and (3) migratory/temporary settling into the sector. The article suggests a theory about lifelong ‘travelling’ when entering women’s work: a continuing process of negotiating work options within a specific historical sector context, the intersection of gender and migration being part of this.     

Modified inference about the mean of the exponential distribution using moving extreme ranked set sampling

The maximum likelihood estimator (MLE) and the likelihood ratio test (LRT) will be considered for making inference about the scale parameter of the exponential distribution in case of moving extreme ranked set sampling (MERSS). The MLE and LRT can not be written in closed form. Therefore, a modification of the MLE using the technique suggested by Maharota and Nanda (Biometrika 61:601–606, 1974) will be considered and this modified estimator will be used to modify the LRT to get a test in closed form for testing a simple hypothesis against one sided alternatives. The same idea will be used to modify the most powerful test (MPT) for testing a simple hypothesis versus a simple hypothesis to get a test in closed form for testing a simple hypothesis against one sided alternatives. Then it appears that the modified estimator is a good competitor of the MLE and the modified tests are good competitors of the LRT using MERSS and simple random sampling (SRS).     

Evaluating modified generalized information criterion in presence of multicollinearity

When there are many explanatory variables in the regression model, there is a chance that some of these are intercorrelated. This is where the problem of multicollinearity creeps in due to which precision and accuracy of the coefficients is marred, and the quest to find the best model becomes tedious. To tackle such a situation, Model selection criteria are applied for selecting the best model that fits the data. Current study focuses on the evaluation of the four unmodified and four modified versions of generalized information criteria—Akaike Information Criterion, Schwarz's Bayes Information Criteria, Hannan-Quinn Information Criterion, and Akaike Information Criterion corrected for small samples. A simulation study using SAS software was carried out in order to compare the unmodified and modified versions of the generalized information criteria and to discover the best version amongst the four modified model selection criteria, for identifying the best model, when the collinearity assumption is violated. For the proposed simulation, two samples of size 50 and 100, for three explanatory variables X₁, X₂, and X₃, are drawn from Normal distribution. Two situations of collinearity violations between X₁ and X₂ are looked into, first when ρ = 0.6 and second when ρ = 0.8. The outcomes of the simulations are displayed in the tables along with visual representations. The results revealed that modified versions of the generalized information criteria are more sensitive in identifying models marred with high multicollinearity as compared to the unmodified generalized information criteria.