Bayesian Nonparametric Calibration and Combination of Predictive Distributions

We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan, R. and Gneiting, T. (2010) and Gneiting, T. and Ranjan, R. (2013), we use infinite beta mixtures for the calibration. The proposed Bayesian nonparametric approach takes advantage of the flexibility of Dirichlet process mixtures to achieve any continuous deformation of linearly combined predictive distributions. The inference procedure is based on Gibbs sampling and allows accounting for uncertainty in the number of mixture components, mixture weights, and calibration parameters. The weak posterior consistency of the Bayesian nonparametric calibration is provided under suitable conditions for unknown true density. We study the methodology in simulation examples with fat tails and multimodal densities and apply it to density forecasts of daily S&P returns and daily maximum wind speed at the Frankfurt airport.Comment: arXiv admin note: text overlap with arXiv:1305.2026 by other author

Measuring sovereign contagion in Europe

This paper analyzes sovereign risk shift-contagion, i.e. positive and significant changes in the propagation mechanisms, using bond yield spreads for the major eurozone countries. By emphasizing the use oftwo econometric approaches based on quantile regressions (standard quantile regression and Bayesianquantile regression with heteroskedasticity) we find that the propagation of shocks in euro\u2019s bond yieldspreads shows almost no presence of shift-contagion in the sample periods considered (2003\u20132006,Nov. 2008\u2013Nov. 2011, Dec. 2011\u2013Apr. 2013). Shock transmission is no different on days with big spreadchanges and small changes. This is the case even though a significant number of the countries in our sample have been extremely affected by their sovereign debt and fiscal situations. The risk spillover amongthese countries is not affected by the size or sign of the shock, implying that so far contagion has remainedsubdued. However, the US crisis does generate a change in the intensity of the propagation of shocks inthe eurozone between the 2003\u20132006 pre-crisis period and the Nov. 2008\u2013Nov. 2011 post-Lehman one,but the coefficients actually go down, not up! All the increases in correlation we have witnessed overthe last years come from larger shocks and the heteroskedasticity in the data, not from similar shockspropagated with higher intensity across Europe. These surprising, but robust, results emerge becausethis is the first paper, to our knowledge, in which a Bayesian quantile regression approach allowing forheteroskedasticity is used to measure contagion. This methodology is particularly well-suited to dealwith nonlinear and unstable transmission mechanisms especially when asymmetric responses to signand size are suspected

Is There a Biological Basis for Treatment of Fibrodysplasia Ossificans Progressiva with Rosiglitazone? Potential Benefits and Undesired Effects

Thiazolidinediones (TZDs), among which Rosiglitazone, are known agonists of the peroxisome-proliferator-activated receptor γ (PPARγ) commonly used for treatment of hyperglycemia. A recently published article describing a case report on a patient affected by Fibrodysplasia Ossificans Progressiva (FOP) treated with Rosiglitazone has prompted interest for careful analysis of the rational basis of such treatment. This article reviews the effects of PPARγ agonists in relationship with various pathogenic steps that occur during the course of FOP by reviewing the particularly rich literature on the effects of Rosiglitazone, to underscore their relevance to FOP and to consider possible adverse effects

Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox

This paper presents the Matlab package DeCo (Density Combination) which is based on the paper by Billio et al. (2013) where a constructive Bayesian approach is presented for combining predictive densities originating from different models or other sources of information. The combination weights are time-varying and may depend on past predictive forecasting performances and other learning mechanisms. The core algorithm is the function DeCo which applies banks of parallel Sequential Monte Carlo algorithms to filter the time-varying combination weights. The DeCo procedure has been implemented both for standard CPU computing and for Graphical Process Unit (GPU) parallel computing. For the GPU implementation we use the Matlab parallel computing toolbox and show how to use General Purposes GPU computing almost effortless. This GPU implementation comes with a speed up of the execution time up to seventy times compared to a standard CPU Matlab implementation on a multicore CPU. We show the use of the package and the computational gain of the GPU version, through some simulation experiments and empirical application

Identification of reference genes for quantitative PCR during C3H10T1/2 chondrogenic differentiation

C3H10T1/2, a mouse mesenchymal stem cell line, is a well-known in vitro model of chondrogenesis that can be easily employed to recapitulate some of the mechanisms intervening in this process. Moreover, these cells can be used to validate the effect of candidate molecules identified by high throughput screening approaches applied to the development of targeted therapy for human disorders in which chondrogenic differentiation may be involved, as in conditions characterized by heterotopic endochondral bone formation. Chondrogenic differentiation of C3H10T1/2 cells can be monitored by applying quantitative polymerase chain reaction (qPCR), one of the most sensitive methods that allows detection of small dynamic changes in gene expression between samples obtained under different experimental conditions. In this work, we have used qPCR to monitor the expression of specific markers during chondrogenic differentiation of C3H10T1/2 cells in micromass cultures. Then we have applied the geNorm approach to identify the most stable reference genes suitable to get a robust normalization of the obtained expression data. Among 12 candidate reference genes (Ap3d1, Csnk2a2, Cdc40, Fbxw2, Fbxo38, Htatsf1, Mon2, Pak1ip1, Zfp91, 18S, ActB, GAPDH) we identified Mon2 and Ap3d1 as the most stable ones during chondrogenesis. ActB, GAPDH and 18S, the most commonly used in the literature, resulted to have an expression level too high compared to the differentiation markers (Sox9, Collagen type 2a1, Collagen type 10a1 and Collagen type 1a1), therefore are actually less recommended for these experimental conditions. In conclusion, we identified nine reference genes that can be equally used to obtain a robust normalization of the gene expression variation during the C3H10T1/2 chondrogenic differentiation

Nuclear Run-On Assay Using Biotin Labeling, Magnetic Bead Capture and Analysis by Fluorescence-Based RT-PCR

In this report, we present a fluorescencebased approach to the assessment of cellular gene expression and transcription rates. Nuclear run-on was performed by supplying biotin-16-UTP to nuclei, and labeled transcripts were bound to streptavidin-coated magnetic beads. Total cDNA was then synthesized by means of random hexamer primed reverse transcription of captured molecules. To monitor transcript abundance in cDNA, both from nuclear run-on and total RNA, we propose a semiquantitative PCR approach based on the use of fluorescent primers

Bayesian Calibration of Generalized Pools of Predictive Distributions

Decision-makers often consult different experts to build reliable forecasts on variables of interest. Combining more opinions and calibrating them to maximize the forecast accuracy is consequently a crucial issue in several economic problems. This paper applies a Bayesian beta mixture model to derive a combined and calibrated density function using random calibration functionals and random combination weights. In particular, it compares the application of linear, harmonic and logarithmic pooling in the Bayesian combination approach. The three combination schemes, i.e., linear, harmonic and logarithmic, are studied in simulation examples with multimodal densities and an empirical application with a large database of stock data. All of the experiments show that in a beta mixture calibration framework, the three combination schemes are substantially equivalent, achieving calibration, and no clear preference for one of them appears. The financial application shows that the linear pooling together with beta mixture calibration achieves the best results in terms of calibrated forecast

Combining Predictive Densities using Bayesian Filtering with Applications to US Economics Data

Using a Bayesian framework this paper provides a multivariate combination approach to prediction based on a distributional state space representation of predictive densities from alternative models. In the proposed approach the model set can be incomplete. Several multivariate time-varying combination strategies are introduced. In particular, a weight dynamics driven by the past performance of the predictive densities is considered and the use of learning mechanisms. The approach is assessed using statistical and utility-based performance measures for evaluating density forecasts of US macroeconomic time series and of surveys of stock market prices

Bayesian Combinations of Stock Price Predictions with an Application to the Amsterdam Exchange Index

We summarize the general combination approach by Billio et al. [2010]. In the combination model the weights follow logistic autoregressive processes, change over time and their dynamics are possible driven by the past forecasting performances of the predictive densities. For illustrative purposes we apply it to combine White Noise and GARCH models to forecast the Amsterdam Exchange index and use the combined predictive forecasts in an investment asset allocation exercise

Transfection of the mutant MYH9 cDNA reproduces the most typical cellular phenotype of MYH9-related disease in different cell lines

ABSTRACT: BACKGROUND: Heterozygous mutations of MYH9, encoding the Non-Muscular Myosin Heavy Chain-IIA (NMMHC-IIA), cause a complex disorder named MYH9-related disease, characterized by a combination of different phenotypic features. At birth, patients present platelet macrocytosis, thrombocytopenia and leukocyte inclusions containing NMMHC-IIA. Moreover, later in life some of them develop the additional features of sensorineural hearing loss, cataracts and/or glomerulonephritis that sometimes leads to end stage renal failure. RESULTS: To clarify the mechanism by which the mutant NMMHC-IIA could cause phenotypic anomalies at the cellular level, we examined the effect of transfection of the full-length mutated D1424H MYH9 cDNAs. We have observed, by confocal microscopy, abnormal distribution of the protein and formation of rod-like aggregates reminiscent of the leukocyte inclusions found in patients. Co-transfection of differently labeled wild-type and mutant full-length cDNAs showed the simultaneous presence of both forms of the protein in the intracellular aggregates. CONCLUSION: These findings suggest that the NMMHC-IIA mutated in position 1424 is able to interact with the WT form in living cells, despite part of the mutant protein precipitates in non-functional aggregates. Transfection of the entire WT or mutant MYH9 in cell lines represents a powerful experimental model to investigate consequences of MYH9 mutations