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.

In-situ absorption and fluorescence studies of electrogenerated species

Imperial Users onl

Modeling Spatially-Referenced MALDI Imaging Data Using a Process Convolution Approach

Matrix-assisted laser desorption/ionization Fourier-transform ion-cyclotron resonance (MALDI FT-ICR) imaging mass spectrometry (IMS) technology allows researchers to measure the abundance of ionized fragments over a two-dimensional space. Despite advances in IMS technology, methods used to analyze such data have lagged. In particular, the variability in IMS data can be attributed to both spatial and random sources. Additionally, the frequency of masses with high proportions of zero abundance measures is often quite large. To address these issues, we automate a procedure to account for spatial variability across multiple regions of interest. Using that procedure, we then develop and propose log-linear regression models facilitating group-level comparisons of ionized fragment abundance, which further account for both the data\u27s spatial structure and excess zeros. Our regression models, while accounting for the spatial structure in the same way, differ in their assumptions about the nature of the zeros, in particular whether they are accurately measured zeros or left-censored observations. We evaluate our models using simulated data and compare performance to approaches that account for the spatial information with differing complexity. We demonstrate that our methods maintain lower type I error rates and higher coverage compared with other approaches. These trends become more pronounced with increasing proportions of zeros, whether those zeros be true zero abundance measures or censored observations. We apply our models to a study examining glycosylation patterns in metastatic breast cancer. We identify N-glycans with differential abundance between primary and secondary tumor tissues, as well tissues stained negative and positive for tumor-associated macrophages. Upon classifying N-glycans into functional groups, we identify patterns that suggest underlying changes in enzymatic activity. Lastly, we develop the R package imagingPC that utilizes our methods to make them accessible to investigators. The R package distills our methods into a small set of functions that require limited knowledge of R. In addition to the base functions that use our methods, we incorporate functions that make our approach transparent and allow users to assess model assumptions

Bootstrap-Based Improvements for Inference with Clustered Errors

Microeconometrics researchers have increasingly realized the essential need to account for any within-group dependence in estimating standard errors of regression parameter estimates. The typical preferred solution is to calculate cluster-robust or sandwich standard errors that permit quite general heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. In applications with few (5-30) clusters, standard asymptotic tests can over-reject considerably. We investigate more accurate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the much-cited differences-in-differences example of Bertrand, Mullainathan and Duflo (2004). In situations where standard methods lead to rejection rates in excess of ten percent (or more) for tests of nominal size 0.05, our methods can reduce this to five percent. In principle a pairs cluster bootstrap should work well, but in practice a Wild cluster bootstrap performs better.clustered errors; random effects; cluster robust; sandwich; bootstrap; bootstrap-t; clustered bootstrap; pairs bootstrap; wild bootstrap.

Brill-Noether theory of squarefree modules supported on a graph

We investigate the analogy between squarefree Cohen-Macaulay modules supported on a graph and line bundles on a curve. We prove a Riemann-Roch theorem, we study the Jacobian and gonality of a graph, and we prove Clifford's theorem.Comment: Major revision, new author added, paper restructured, results correcte

Juvenile Justice System Involvement and the Transition to Early Adulthood: Does Direct Intervention Help or Harm?

The present study examines the effects of the juvenile justice system on youth as they transition to early adulthood. The present study adds to the literature by incorporating comparison groups of youths and by testing multiple explanations of recidivism: Labeling Theory and the Life Course perspective. In total, 267 adults were recruited via online survey and sorted into three groups: Externalizing Behavior and Juvenile Justice Involved, Externalizing Behavior and Not Juvenile Justice Involved, and Not Externalizing and Not Juvenile Justice Involved. In addition to demographics, participants completed measures of past externalizing behaviors, past juvenile justice involvement, social disadvantage, deviant peer affiliations in youth and currently, current general mental health and psychopathy, and adult criminal behavior. Labeling did not mediate the relationship between juvenile justice involvement and adult crime. Social disadvantage did not moderate the relationships between juvenile justice involvement and distal psychosocial outcomes. Deviant peer relationships did not mediate the relationship between juvenile justice involvement and adult crime. Youth externalizing behaviors was associated with several distal psychosocial outcomes including deviant peer relationships, psychiatric symptoms, and adult crime. Juvenile justice involvement was significantly negatively related to educational attainment. Implications for future research are discussed

When do Men Perceive that \u27No\u27 Means \u27Yes\u27?: Effects of Alcohol and Men\u27s Expectancies of Intoxicated Women\u27s Sexual Desire and Vulnerability on Sexual Aggression

This study examined the independent and interactive effects of acute alcohol consumption, perceived alcohol consumption of a female confederate, and distal alcohol expectancies of intoxicated women, on sexual aggression. Participants were a diverse community sample (54% African-American) of heterosexual males (N = 156) between 21 and 35 years of age who were recruited to complete the study with a male friend and an ostensibly single, heterosexual female who reported a strong dislike of sexual content in the media. Sexual aggression was measured utilizing a well-validated laboratory paradigm in which participants viewed a sexually explicit or non-sexually explicit video clip as part of a contrived media rating task and made individual choices of which video clip to show the female confederate. Sexual aggression was operationalized as selection of the sexually explicit video, as opposed to the non-sexually explicit video. Results demonstrated that acute alcohol consumption, perceived female alcohol consumption, and distal alcohol expectancies of women’s vulnerability to sexual coercion and sexual drive while intoxicated, were not significantly related to sexual aggression utilizing the current paradigm. Post-hoc analyses revealed that the primary predictor variables were significantly related to participants’ perceived distress of a female confederate following an act of sexual aggression. Discussion focused on understanding what factors may have been relevant in understanding why the primary predictor variables were not significantly related to sexual aggression utilizing the current paradigm. Finally, clinical implications were explored in addressing a lack of perceived distress in potential female victims by individuals who endorsed higher levels of distal alcohol expectancies of intoxicated women’s vulnerability to sexual coercion and what potential interventions be utilized clinically