Monte Carlo Estimation of the Density of the Sum of Dependent Random Variables

We study an unbiased estimator for the density of a sum of random variables that are simulated from a computer model. A numerical study on examples with copula dependence is conducted where the proposed estimator performs favourably in terms of variance compared to other unbiased estimators. We provide applications and extensions to the estimation of marginal densities in Bayesian statistics and to the estimation of the density of sums of random variables under Gaussian copula dependence

Approximate Bayesian Computations to fit and compare insurance loss models

Approximate Bayesian Computation (ABC) is a statistical learning technique to calibrate and select models by comparing observed data to simulated data. This technique bypasses the use of the likelihood and requires only the ability to generate synthetic data from the models of interest. We apply ABC to fit and compare insurance loss models using aggregated data. We present along the way how to use ABC for the more common claim counts and claim sizes data. A state-of-the-art ABC implementation in Python is proposed. It uses sequential Monte Carlo to sample from the posterior distribution and the Wasserstein distance to compare the observed and synthetic data

Efficient Simulation for Dependent Rare Events with Applications to Extremes

We consider the general problem of estimating probabilities which arise as a union of dependent events. We propose a flexible series of estimators for such probabilities, and describe variance reduction schemes applied to the proposed estimators. We derive efficiency results of the estimators in rare-event settings, in particular those associated with extremes. Finally, we examine the performance of our estimators in numerical examples

Market rationality and dividend announcements

We investigate stock market rationality by examining the timeliness and unbiasedness of the market's response to dividend announcements. Our initial findings for market timeliness show a sluggish market reaction to dividend announcements; however, when the ex-dividend effect is controlled for, we find no evidence of a sluggish market reaction. We examine the unbiasedness of the market's response by testing whether the net announcement effect across a sample that is devoid of ex-post selection bias sums to zero. We observe a significant positive net announcement effect and examine several plausible conjectures for this puzzling phenomenon, but none provides a satisfactory explanation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/25487/1/0000028.pd

Regulatory (pan-)genome of an obligate intracellular pathogen in the PVC superphylum.

Like other obligate intracellular bacteria, the Chlamydiae feature a compact regulatory genome that remains uncharted owing to poor genetic tractability. Exploiting the reduced number of transcription factors (TFs) encoded in the chlamydial (pan-)genome as a model for TF control supporting the intracellular lifestyle, we determined the conserved landscape of TF specificities by ChIP-Seq (chromatin immunoprecipitation-sequencing) in the chlamydial pathogen Waddlia chondrophila. Among 10 conserved TFs, Euo emerged as a master TF targeting >100 promoters through conserved residues in a DNA excisionase-like winged helix-turn-helix-like (wHTH) fold. Minimal target (Euo) boxes were found in conserved developmentally-regulated genes governing vertical genome transmission (cytokinesis and DNA replication) and genome plasticity (transposases). Our ChIP-Seq analysis with intracellular bacteria not only reveals that global TF regulation is maintained in the reduced regulatory genomes of Chlamydiae, but also predicts that master TFs interpret genomic information in the obligate intracellular α-proteobacteria, including the rickettsiae, from which modern day mitochondria evolved

Genetic and Computational Identification of a Conserved Bacterial Metabolic Module

We have experimentally and computationally defined a set of genes that form a conserved metabolic module in the α-proteobacterium Caulobacter crescentus and used this module to illustrate a schema for the propagation of pathway-level annotation across bacterial genera. Applying comprehensive forward and reverse genetic methods and genome-wide transcriptional analysis, we (1) confirmed the presence of genes involved in catabolism of the abundant environmental sugar myo-inositol, (2) defined an operon encoding an ABC-family myo-inositol transmembrane transporter, and (3) identified a novel myo-inositol regulator protein and cis-acting regulatory motif that control expression of genes in this metabolic module. Despite being encoded from non-contiguous loci on the C. crescentus chromosome, these myo-inositol catabolic enzymes and transporter proteins form a tightly linked functional group in a computationally inferred network of protein associations. Primary sequence comparison was not sufficient to confidently extend annotation of all components of this novel metabolic module to related bacterial genera. Consequently, we implemented the Graemlin multiple-network alignment algorithm to generate cross-species predictions of genes involved in myo-inositol transport and catabolism in other α-proteobacteria. Although the chromosomal organization of genes in this functional module varied between species, the upstream regions of genes in this aligned network were enriched for the same palindromic cis-regulatory motif identified experimentally in C. crescentus. Transposon disruption of the operon encoding the computationally predicted ABC myo-inositol transporter of Sinorhizobium meliloti abolished growth on myo-inositol as the sole carbon source, confirming our cross-genera functional prediction. Thus, we have defined regulatory, transport, and catabolic genes and a cis-acting regulatory sequence that form a conserved module required for myo-inositol metabolism in select α-proteobacteria. Moreover, this study describes a forward validation of gene-network alignment, and illustrates a strategy for reliably transferring pathway-level annotation across bacterial species

CovR-Controlled Global Regulation of Gene Expression in Streptococcus mutans

CovR/S is a two-component signal transduction system (TCS) that controls the expression of various virulence related genes in many streptococci. However, in the dental pathogen Streptococcus mutans, the response regulator CovR appears to be an orphan since the cognate sensor kinase CovS is absent. In this study, we explored the global transcriptional regulation by CovR in S. mutans. Comparison of the transcriptome profiles of the wild-type strain UA159 with its isogenic covR deleted strain IBS10 indicated that at least 128 genes (∼6.5% of the genome) were differentially regulated. Among these genes, 69 were down regulated, while 59 were up regulated in the IBS10 strain. The S. mutans CovR regulon included competence genes, virulence related genes, and genes encoded within two genomic islands (GI). Genes encoded by the GI TnSmu2 were found to be dramatically reduced in IBS10, while genes encoded by the GI TnSmu1 were up regulated in the mutant. The microarray data were further confirmed by real-time RT-PCR analyses. Furthermore, direct regulation of some of the differentially expressed genes was demonstrated by electrophoretic mobility shift assays using purified CovR protein. A proteomic study was also carried out that showed a general perturbation of protein expression in the mutant strain. Our results indicate that CovR truly plays a significant role in the regulation of several virulence related traits in this pathogenic streptococcus