A Permutation Approach for Selecting the Penalty Parameter in Penalized Model Selection

We describe a simple, efficient, permutation based procedure for selecting the penalty parameter in the LASSO. The procedure, which is intended for applications where variable selection is the primary focus, can be applied in a variety of structural settings, including generalized linear models. We briefly discuss connections between permutation selection and existing theory for the LASSO. In addition, we present a simulation study and an analysis of three real data sets in which permutation selection is compared with cross-validation (CV), the Bayesian information criterion (BIC), and a selection method based on recently developed testing procedures for the LASSO

Climate Lecture 4: Atmospheric Radiation

The climate system is well known for its great complexity and complex interactions that involve dynamic, thermodynamic, radiative, chemical, biological and human-driven processes. This view of the climate system has emerged from detailed measurements, meticulous record keeping, and theoretical analyses arising from, and made possible by the science and technology revolution that greatly advanced our understanding the role of physical processes that operate in the global climate system. These measurements also show very clearly that the global surface temperature has been rising over the past century, and that this is a consequence of human industrial activity

Death awareness and personal change

Fear of death is cited by some psychotherapists as a major factor inhibiting the process of personal change. At the same time there is evidence from many different sources that awareness of one's mortality can lead to positive changes in attitude and behaviour. In the current study eight subjects who have come close to death are interviewed and a detailed examination is made of their life-threatening experience (LTE) and their prior and subsequent attitudes towards life and death in an attempt to understand the factors involved in the personal change resulting from their experience. All the subjects describe significant changes in their attitudes following their LTE. Some report both positive and negative changes, whereas others see the changes as predominantly or wholely positive. The main finding is that the one factor which all these subjects have in common is the integration into their perception of themselves and of their world of an awareness of their personal mortality. Some implications of the results are discussed and directions for future research are outlined

Joint estimation of multiple dependent Gaussian graphical models with applications to mouse genomics

Gaussian graphical models are widely used to represent conditional dependence among random variables. In this paper, we propose a novel estimator for data arising from a group of Gaussian graphical models that are themselves dependent. A motivating example is that of modeling gene expression collected on multiple tissues from the same individual: here the multivariate outcome is affected by dependencies acting not only at the level of the specific tissues, but also at the level of the whole body; existing methods that assume independence among graphs are not applicable in this case. To estimate multiple dependent graphs, we decompose the problem into two graphical layers: the systemic layer, which affects all outcomes and thereby induces cross-graph dependence, and the category-specific layer, which represents graph-specific variation. We propose a graphical EM technique that estimates both layers jointly, establish estimation consistency and selection sparsistency of the proposed estimator, and confirm by simulation that the EM method is superior to a simple one-step method. We apply our technique to mouse genomics data and obtain biologically plausible results

Суточные колебания времени ожеребения

http://tartu.ester.ee/record=b1276783~S1*es

Morbus medicamentosus, arstlik kollegiaalsus ja patsiendi autonoomia

Eesti Arst 2015; 94(3):186–18

CenteringPregnancy®: Reducing Infant Mortality with Group Prenatal Care – A Policy Recommendation

North Carolina’s infant mortality rate remains high, largely as a result of preterm birth and persistent racial and ethnic disparities in health outcomes. While the content and style of routine prenatal care has for the most part remained the same for several decades, CenteringPregnancy®, a new creative model of prenatal care conducted in groups, has been growing in popularity. The CenteringPregnancy® program satisfies all North Carolina Division of Public Health prenatal care and education requirements and recommendations. As programs are implemented across the country, the benefits are becoming increasingly apparent through analysis of perinatal outcomes, and quantitative and qualitative research studies. This model of group prenatal care been shown to reduce the risk of preterm birth, lengthen gestation and increase birth weight of infants that are born preterm, increase the rate of breastfeeding initiation, and rate highly in terms of participant and provider satisfaction. It also holds promise for reaching the women at highest risk of poor outcomes by taking a life-course perspective, providing social support and encouraging community building, and has the potential to reduce racial and ethnic disparities in infant mortality. CenteringPregnancy® compares favorably with individual prenatal care from a financial perspective. This policy recommendation proposes that CenteringPregnancy® be considered the new gold standard for prenatal care in North Carolina.Master of Public Healt

A Bayesian model selection approach to mediation analysis.

Genetic studies often seek to establish a causal chain of events originating from genetic variation through to molecular and clinical phenotypes. When multiple phenotypes share a common genetic association, one phenotype may act as an intermediate for the genetic effects on the other. Alternatively, the phenotypes may be causally unrelated but share genetic loci. Mediation analysis represents a class of causal inference approaches used to determine which of these scenarios is most plausible. We have developed a general approach to mediation analysis based on Bayesian model selection and have implemented it in an R package, bmediatR. Bayesian model selection provides a flexible framework that can be tailored to different analyses. Our approach can incorporate prior information about the likelihood of models and the strength of causal effects. It can also accommodate multiple genetic variants or multi-state haplotypes. Our approach reports posterior probabilities that can be useful in interpreting uncertainty among competing models. We compared bmediatR with other popular methods, including the Sobel test, Mendelian randomization, and Bayesian network analysis using simulated data. We found that bmediatR performed as well or better than these alternatives in most scenarios. We applied bmediatR to proteome data from Diversity Outbred (DO) mice, a multi-parent population, and demonstrate the power of mediation with multi-state haplotypes. We also applied bmediatR to data from human cell lines to identify transcripts that are mediated through or are expressed independently from local chromatin accessibility. We demonstrate that Bayesian model selection provides a powerful and versatile approach to identify causal relationships in genetic studies using model organism or human data