Multivariate integer-valued autoregressive models applied to earthquake counts

In various situations in the insurance industry, in finance, in epidemiology, etc., one needs to represent the joint evolution of the number of occurrences of an event. In this paper, we present a multivariate integer-valued autoregressive (MINAR) model, derive its properties and apply the model to earthquake occurrences across various pairs of tectonic plates. The model is an extension of Pedelis & Karlis (2011) where cross autocorrelation (spatial contagion in a seismic context) is considered. We fit various bivariate count models and find that for many contiguous tectonic plates, spatial contagion is significant in both directions. Furthermore, ignoring cross autocorrelation can underestimate the potential for high numbers of occurrences over the short-term. Our overall findings seem to further confirm Parsons & Velasco (2001)

On the Variability and Predictability of Eastern Pacific Tropical Cyclone Activity

Variability in tropical cyclone activity in the eastern Pacific basin has been linked to a wide range of climate factors, yet the dominant factors driving this variability have yet to be identified. Using Poisson regressions and a track clustering method, the authors analyze and compare the climate influence on cyclone activity in this region. The authors show that local sea surface temperature and upper-ocean heat content as well as large-scale conditions in the northern Atlantic are the dominant influence in modulating eastern North Pacific tropical cyclone activity. The results also support previous findings suggesting that the influence of the Atlantic Ocean occurs through changes in dynamical conditions over the eastern Pacific. Using model selection algorithms, the authors then proceed to construct a statistical model of eastern Pacific tropical cyclone activity. The various model selection techniques used agree in selecting one predictor from the Atlantic (northern North Atlantic sea surface temperature) and one predictor from the Pacific (relative sea surface temperature) to represent the best possible model. Finally, we show that this simple model could have predicted the anomalously high level of activity observed in 2014

Impact of reanalysis boundary conditions on downscaled Atlantic hurricane activity

Climate models are capable of producing features similar to tropical cyclones, but typically display strong biases for many of the storm physical characteristics due to their relatively coarse resolution compared to the size of the storms themselves. One strategy that has been adopted to circumvent this limitation is through the use of a hybrid downscaling technique, wherein a large set of synthetic tracks are created by seeding disturbances in the large-scale environment. Here, we evaluate the ability of this technique at reproducing many of the characteristics of the recent North Atlantic hurricane activity as well as its sensitivity to the choice of the reanalysis dataset used as boundary conditions. In particular, we show that the geographical and intensity distributions are well reproduced, but that the technique has difficulty capturing the large difference in activity observed between the most recent active and quiescent phase. Although the signal is somewhat reduced compared to observation, the technique also detects a significant decrease in the intensification rate of hurricanes near the coastal US during the active phase compared to the quiescent phase. Finally, the influence of the El Niño Southern Oscillation on hurricane activity is generally well captured as well, but the technique fails to reproduce the increase in activity over the western part of the basin during Modoki El Niños.We would like to thank NOAA’s National Centers for Environmental Information for making the IBTrACS data available. JPB and LPC would like to acknowledge the financial support from the Ministerio de Economa y Competitividad (MINECO; Project GL2014-55764-R). LPC’s contract is co-financed by the MINECO under Juan de la Cierva Incorporacin postdoctoral fellowship number IJCI-2015-23367. MB would like to acknowledge financial support from the Natural Sciences and Engineering Research Council (NSERC) of Canada. Finally, we are grateful to Kerry Emanuel for providing the data as well as some useful feedback, and two anonymous reviewers for their helpful comments.Peer ReviewedPostprint (published version

Maximum likelihood estimation of first-passage structural credit risk models correcting for the survivorship bias

The final publication is available at Elsevier via https://doi.org/10.1016/j.jedc.2018.11.005. © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The survivorship bias in credit risk modeling is the bias that results in parameter estimates when the survival of a company is ignored. We study the statistical properties of the maximum likelihood estimator (MLE) accounting for survivorship bias for models based on the first-passage of the geometric Brownian motion. We find that if we neglect the survivorship bias, then the drift has a positive bias that may not disappear asymptotically. We show that correcting the survivorship bias by conditioning on survival in the likelihood function underestimates the drift. Therefore, we propose a bias correction method for non-iid samples that is first-order unbiased and second-order efficient. The economic impact of neglecting or miscorrecting for the survivorship bias is studied empirically based on a sample of more than 13,000 companies over the period 1980 through 2016 inclusive. Our results point to the important risk of misclassifying a company as solvent or insolvent due to biases in the estimates

Reovirus μ2 protein modulates host cell alternative splicing by reducing protein levels of U5 snRNP core components

Abstract : Mammalian orthoreovirus (MRV) is a doublestranded RNA virus from the Reoviridae family presenting a promising activity as an oncolytic virus. Recent studies have underlined MRV’s ability to alter cellular alternative splicing (AS) during infection, with a limited understanding of the mechanisms at play. In this study, we investigated how MRV modulates AS. Using a combination of cell biology and reverse genetics experiments, we demonstrated that theM1 gene segment, encoding the μ2 protein, is the primary determinant of MRV’s ability to alter AS, and that the amino acid at position 208 in μ2 is critical to induce these changes. Moreover, we showed that the expression of μ2 by itself is sufficient to trigger AS changes, and its ability to enter the nucleus is not required for all these changes. Moreover, we identified core components of the U5 snRNP (i.e. EFTUD2, PRPF8, and SNRNP200) as interactors of μ2 that are required for MRV modulation of AS. Finally, these U5 snRNP components are reduced at the protein level by both MRV infection and μ2 expression. Our findings identify the reduction of U5 snRNP components levels as a new mechanism by which viruses alter cellular AS

Reanalysis of climate influences on Atlantic tropical cyclone activity using cluster analysis

We analyze, using Poisson regressions, the main climate influences on North Atlantic tropical cyclone activity. The analysis is performed using not only various time series of basin‐wide storm counts but also various series of regional clusters, taking into account shortcomings of the hurricane database through estimates of missing storms. The analysis confirms that tropical cyclones forming in different regions of the Atlantic are susceptible to different climate influences. We also investigate the presence of trends in these various time series, both at the basin‐wide and cluster levels, and show that, even after accounting for possible missing storms, there remains an upward trend in the eastern part of the basin and a downward trend in the western part. Using model selection algorithms, we show that the best model of Atlantic tropical cyclone activity for the recent past is constructed using Atlantic sea surface temperature and upper tropospheric temperature, while for the 1878–2015 period, the chosen covariates are Atlantic sea surface temperature and El Niño–Southern Oscillation. We also note that the presence of these artificial trends can impact the selection of the best covariates. If the underlying series shows an upward trend, then the mean Atlantic sea surface temperature captures both interannual variability and the upward trend, artificial or not. The relative sea surface temperature is chosen instead for stationary counts. Finally, we show that the predictive capability of the statistical models investigated is low for U.S. landfalling hurricanes but can be considerably improved when forecasting combinations of clusters whose hurricanes are most likely to make landfall

Global profiling of alternative RNA splicing events provides insights into molecular differences between various types of hepatocellular carcinoma

Protein families encoded by transcripts that are differentially spliced in various types of HCC. Table S2. Bioinformatical prediction of functional changes caused by some of ASEs identified. Table S3. List of tumor suppressors for which AS is dysregulated in various types of HCC. Table S4. List of oncogenes for which AS is dysregulated in various types of HCC. Table S5. List of kinases for which AS is dysregulated in various types of HCC. Table S6. List of transcription factors for which AS is dysregulated in various types of HCC. Table S7. List of genes for which AS is dysregulated in all types of HCC. Table S8. List of genes uniquely dysregulated in HBV-associated HCC. Table S9. List of genes uniquely dysregulated in HCV-associated HCC. Table S10. List of genes uniquely dysregulated in HBV&HCV-associated HCC. Table S11. List of genes uniquely dysregulated in virus-free HCC. Figure S1. Characterization of splicing mysregulation in HCC. Figure S2. Characterization of ASEs that are modified in HBV- and HCV-associated HCC. Figure S3. AS modifications in transcripts encoded by kinases and transcriptions factores in HBV- and HCV-associated HCC. Figure S4. Global profiling of ASE modifications in both HBV&HCV-associated HCC and virus-free-associated HCC. Figure S5. RNA splicing factors in HCC. Figure S6. Modifications to AS of 96 transcripts in response to knockdown of splicing factors with specific siRNAs (PDF 6675 kb