Robust Inference with Clustered Data

In this paper we survey methods to control for regression model error that is correlated within groups or clusters, but is uncorrelated across groups or clusters. Then failure to control for the clustering can lead to understatement of standard errors and overstatement of statistical significance, as emphasized most notably in empirical studies by Moulton (1990) and Bertrand, Duflo and Mullainathan (2004). We emphasize OLS estimation with statistical inference based on minimal assumptions regarding the error correlation process. Complications we consider include cluster-specific fixed effects, few clusters, multi-way clustering, more efficient feasible GLS estimation, and adaptation to nonlinear and instrumental variables estimators.Cluster robust, random eects, xed eects, dierences in dierences, cluster bootstrap, few clusters, multi-way clusters.

Robust Inference with Clustered Data

In this paper we survey methods to control for regression model error that is correlated within groups or clusters, but is uncorrelated across groups or clusters. Then failure to control for the clustering can lead to understatement of standard errors and overstatement of statistical significance, as emphasized most notably in empirical studies by Moulton (1990) and Bertrand, Duflo and Mullainathan (2004). We emphasize OLS estimation with statistical inference based on minimal assumptions regarding the error correlation process. Complications we consider include cluster-specific fixed effects, few clusters, multi-way clustering, more efficient feasible GLS estimation, and adaptation to nonlinear and instrumental variables estimators.Cluster robust, random effects, fixed effects, differences in differences, cluster bootstrap, few clusters, multi-way clusters.

Community Management of Determined Drinkers

1. The 1970's were times of considerable change in our understanding and beliefs about the nature of 'problem drinking'. The 'Disease Concept of Alcoholism' had been shown to have limited value and no validity. The hazards of inappropriate use of the 'Alcoholism' label had been highlighted

Global sensitivity analysis of an end-to-end marine ecosystem model of the North Sea : factors affecting the biomass of fish and benthos

Comprehensive analysis of parameter and driver sensitivity is key to establishing the credibility of models of complex systems. This is especially so for models of natural systems where experimental manipulation of the real-world to provide controlled validation data is not possible. Models of marine ecosystems fall into this category, but despite the interest in these models for evaluating the effects of climate change and fishing on nutrient fluxes and the abundances of flora and fauna, none have yet been subjected to global sensitivity analysis. Here we present results of both local ‘one-at-a-time’ (OAT), and variance based global sensitivity analyses (GSA) of the fish and fishery aspects of StrathE2E, an end-to-end (nutrients to birds and mammals) ecosystem model of the North Sea. The sensitivity of the model was examined with respect to internal biological parameters, and external drivers related to climate and human activity. The OAT Morris method was first used to screen for factors most influential on model outputs. The Sobol GSA method was then used to calculate quantitative sensitivity indices. The results indicated that the fish and shellfish components of the model (demersal and pelagic fish, filter/deposit and scavenge/carnivore feeding benthos) were influenced by different sets of factors. Harvesting rates were directly influential on demersal and pelagic fish biomasses. Suspension/deposit feeding benthos were directly sensitive to changes in temperature, while the temperature acted indirectly on pelagic fish through the connectivity between model components of the food web. Biomass conversion efficiency was the most important factor for scavenge/carnivorous feeding benthos. The results indicate the primacy of fishing as the most important process affecting total fish biomass, together with varying responses to environmental factors which may be relevant in the context of climate change. The non-linear responses and parameter interactions identified by the analysis also highlight the necessity to use global rather than local methods for the sensitivity analysis of ecosystem models

La représentation des frontières dans le cinéma contemporain : l’imbrication des cultures dans El Mariachi

Douglas M. Cameron démontre, avec le film El Mariachi (1992) de Robert Rodriguez, qu'une nouvelle sorte de héros est apparue dans les films réalisés pour l'Amérique latine: le héros postmoderne d'aventures, qui devient écrivain et transcende sa culture sans la perdre. Le film, qui a bien des points communs avec les autobiographies fictionnelles de la littérature contemporaine (celles de Cortàzar, Fuentes ou de certains romanciers québécois), démontre comment un mariachi devient écrivain après avoir été remplacé par les synthétiseurs des cantinas du Mexique. Le mariachi survit au traumatisme causé par la perte de son idéal (emblématique de la vie le long de la frontière américano-mexicaine) en abandonnant l'institution de la cantina et en orientant sa carrière vers un autre espace collectif, le cinéma. Le changement est une réussite du fait que le mariachi a accepté la technologie afin de devenir le narrateur d'un film dont il est le sujet, afin de devenir (probablement) le premier cinéaste- mariachi. Le film, ainsi, offre au public une image du changement qui valorise l'expérience régionale, communautaire, mais qui s'ouvre en même temps à la discussion, aux débats et à la reconfiguration.Douglas Cameron demonstrates by means of the film by Robert Rodriguez ( El Mariachi, 1992) that a new kind of hero bas evolved in films made for Latin America, the postmodern action hero-one who becomes a writer and transcends his culture without losing it. Much like a fictional autobiography in contemporary literature (e.g. those of Cortàzar, Fuentes or the Québec novelists), the film demonstrates how the mariachi becomes a writer after being replaced by the synthetizer in the cantinas of Mexico. He survives the trauma (emblematic of culture along the U.S.Mexico border) by moving beyond the cantina and entering the service of another collective space, the movie theater, having become the narrator in the film about himself, possibly even the filmmaker. The film, therefore, supplies an image of change within a culture, one that preserves the validity of the local, communal experience and makes it accessible to discussion, appreciation and debate

Leading Indicators of Safety in Nuclear Energy

The purpose of this research was to attempt to determine the key leading indicators of safety in the nuclear energy generation industry. A leading indicator is a metric of safety performance that is not dependent on trending injuries or incidents. This study was conducted by sending surveys to individuals working in the nuclear energy industry, and attempting to rate a set of indicators by their responses. This research was unsuccessful in empirical validation of any of the surveyed indicators, but does indicate that the concept is well known and accepted in the industry, and paves the way for a more in depth study in safety culture and systems in this highly specialized industry. Of the surveyed indicators, three seemed to have a more substantial impact than others according to feedback given. The three indicators are: maintenance of safety critical equipment, ease of use of technical manuals and guides, and relevance of employee training

Developing Statistical Methods for Incorporating Complexity in Association Studies

Genome-wide association studies (GWAS) have identified thousands of genetic variants associated with hundreds of human traits. Yet the common variant model tested by traditional GWAS only provides an incomplete explanation for the known genetic heritability of many traits. Many divergent methods have been proposed to address the shortcomings of GWAS, including most notably the extension of association methods into rarer variants through whole exome and whole genome sequencing. GWAS methods feature numerous simplifications designed for feasibility and ease of use, as opposed to statistical rigor. Furthermore, no systematic quantification of the performance of GWAS across all traits exists. Beyond improving the utility of data that already exist, a more thorough understanding of the performance of GWAS on common variants may elucidate flaws not in the method but rather in its implementation, which may pose a continued or growing threat to the utility of rare variant association studies now underway. This thesis focuses on systematic evaluation and incremental improvement of GWAS modeling. We collect a rich dataset containing standardized association results from all GWAS conducted on quantitative human traits, finding that while the majority of published significant results in the field do not disclose sufficient information to determine whether the results are actually valid, those that do replicate precisely in concordance with their statistical power when conducted in samples of similar ancestry and reporting accurate per-locus sample sizes. We then look to the inability of effectively all existing association methods to handle missingness in genetic data, and show that adapting missingness theory from statistics can both increase power and provide a flexible framework for extending most existing tools with minimal effort. We finally undertake novel variant association in a schizophrenia cohort from a bottleneck population. We find that the study itself is confounded by nonrandom population sampling and identity-by-descent, manifesting as batch effects correlated with outcome that remain in novel variants after all sample-wide quality control. On the whole, these results emphasize both the past and present utility and reliability of the GWAS model, as well as the extent to which lessons from the GWAS era must inform genetic studies moving forward

Bootstrap-Based Improvements for Inference with Clustered Errors

Researchers have increasingly realized the need to account for within-group dependence in estimating standard errors of regression parameter estimates. The usual solution is to calculate cluster-robust standard errors that permit heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. Standard asymptotic tests can over-reject, however, with few (5-30) clusters. We investigate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the example of Bertrand, Duflo and Mullainathan (2004). Rejection rates of ten percent using standard methods can be reduced to the nominal size of five percent using our methods.

Robust Inference with Multi-way Clustering

In this paper we propose a new variance estimator for OLS as well as for nonlinear estimators such as logit, probit and GMM, that provcides cluster-robust inference when there is two-way or multi-way clustering that is non-nested. The variance estimator extends the standard cluster-robust variance estimator or sandwich estimator for one-way clustering (e.g. Liang and Zeger (1986), Arellano (1987)) and relies on similar relatively weak distributional assumptions. Our method is easily implemented in statistical packages, such as Stata and SAS, that already offer cluster-robust standard errors when there is one-way clustering. The method is demonstrated by a Monte Carlo analysis for a two-way random effects model; a Monte Carlo analysis of a placebo law that extends the state-year effects example of Bertrand et al. (2004) to two dimensions; and by application to two studies in the empirical public/labor literature where two-way clustering is present.

Glimpses of Oregon’s Sea Otters

Sea otters are an iconic species in the history of what is now known as Oregon. Their pelts brought great wealth in late eighteenth and nineteenth century China, motivating some of Oregon’s earliest exploration, trade, and contact between Native American and Euro-American people. Over time, hunting eliminated the species from Oregon’s coastal waters. This article provides a broad introduction to the history of Oregon’s now-extinct sea otter population, describing the emergence of the Chinese market that created and sustained the hunt, the British discovery of profits to be made by trading for the pelts, and the rise of American traders. This historical information is placed within the context of sea otter ecology and provides estimates of Oregon’s sea otter population on the eve of the maritime fur trade