One-Step Robust Estimation of Fixed-Effects Panel Data Models

The panel-data regression models are frequently applied to micro-level data, which often suffer from data contamination, erroneous observations, or unobserved heterogeneity. Despite the adverse effects of outliers on classical estimation methods, there are only a few robust estimation methods available for fixed-effect panel data. Aiming at estimation under weak moment conditions, a new estimation approach based on two different data transformation is proposed. Considering several robust estimation methods applied on the transformed data, we derive the finite-sample, robust, and asymptotic properties of the proposed estimators including their breakdown points and asymptotic distribution. The finite-sample performance of the existing and proposed methods is compared by means of Monte Carlo simulations.breakdown point;fixed effects;panel data;robust estimation

One-Step Robust Estimation of Fixed-Effects Panel Data Models

The panel-data regression models are frequently applied to micro-level data, which often suffer from data contamination, erroneous observations, or unobserved heterogeneity. Despite the adverse effects of outliers on classical estimation methods, there are only a few robust estimation methods available for fixed-effect panel data. Aiming at estimation under weak moment conditions, a new estimation approach based on two different data transformation is proposed. Considering several robust estimation methods applied on the transformed data, we derive the finite-sample, robust, and asymptotic properties of the proposed estimators including their breakdown points and asymptotic distribution. The finite-sample performance of the existing and proposed methods is compared by means of Monte Carlo simulations

One-Step Robust Estimation of Fixed-Effects Panel Data Models

The panel-data regression models are frequently applied to micro-level data, which often suffer from data contamination, erroneous observations, or unobserved heterogeneity. Despite the adverse effects of outliers on classical estimation methods, there are only a few robust estimation methods available for fixed-effect panel data. Aiming at estimation under weak moment conditions, a new estimation approach based on two different data transformation is proposed. Considering several robust estimation methods applied on the transformed data, we derive the finite-sample, robust, and asymptotic properties of the proposed estimators including their breakdown points and asymptotic distribution. The finite-sample performance of the existing and proposed methods is compared by means of Monte Carlo simulations.

A Bayesian Networks Approach to Operational Risk

A system for Operational Risk management based on the computational paradigm of Bayesian Networks is presented. The algorithm allows the construction of a Bayesian Network targeted for each bank using only internal loss data, and takes into account in a simple and realistic way the correlations among different processes of the bank. The internal losses are averaged over a variable time horizon, so that the correlations at different times are removed, while the correlations at the same time are kept: the averaged losses are thus suitable to perform the learning of the network topology and parameters. The algorithm has been validated on synthetic time series. It should be stressed that the practical implementation of the proposed algorithm has a small impact on the organizational structure of a bank and requires an investment in human resources limited to the computational area

Ultrametric identities in glassy models of natural evolution

Spin-glasses constitute a well-grounded framework for evolutionary models. Of particular interest for (some of) these models is the lack of self-averaging of their order parameters (e.g. the Hamming distance between the genomes of two individuals), even in asymptotic limits, much as like what happens to the overlap between the configurations of two replica in mean-field spin-glasses. In the latter, this lack of self-averaging is related to a peculiar behavior of the overlap fluctuations, as described by the Ghirlanda–Guerra identities and by the Aizenman–Contucci polynomials, that cover a pivotal role in describing the ultrametric structure of the spin-glass landscape. As for evolutionary mod- els, such identities may therefore be related to a taxonomic classification of individuals, yet a full investigation on their validity is missing. In this paper, we study ultrametric identities in simple cases where solely random mutations take place, while selective pressure is absent, namely in flat landscape models. In particular, we study three paradigmatic models in this setting: the one parent model (which, by construction, is ultrametric at the level of single individu- als), the homogeneous population model (which is replica symmetric), and the species formation model (where a broken-replica scenario emerges at the level of species). We find analytical and numerical evidence that in the first and in the third model nor the Ghirlanda–Guerra neither the Aizenman–Contucci constraints hold, rather a new class of ultrametric identities is satisfied; in the second model all these constraints hold trivially. Very preliminary results on a real biological human genome derived by The 1000 Genome Project Consortium and on two artificial human genomes (generated by two different types neural networks) seem in better agreement with these new identities rather than the classic ones

Comparative antiviral activity of integrase inhibitors in human monocyte-derived macrophages and lymphocytes

The activity of raltegravir and 4 other integrase inhibitors (MK-2048, L870,810, IN2, and IN5) was investigated in primary human macrophages, PBMC and C8166-lymphocytic T cells, in order to determine their relative potency and efficacy in different cellular systems of HIV infection. Raltegravir showed better protective efficacy in all cell types; MK-2048, L870,810 and IN5 showed a potent anti-HIV-1 activity in macrophages, while in lymphocytes only MK-2048 and L870,810 showed an inhibitory effect comparable to raltegravir. IN2 was a poorly effective anti-HIV-1 compound in all cellular systems. All effective integrase inhibitors exhibited a potent antiviral activity against both X4 and R5 HIV-1 strains. In general, raltegravir, MK-2048, L870,810 and IN5 showed anti HIV activity similar or slightly higher in macrophages compared to PBMC and C8166 T cells: for MK-2048, the EC(50) was 0.4, 0.9, 11.5nM in macrophages, in PBMCs and T cells, respectively; for L870,810, the EC(50) was 1.5, 14.3, and 10.6nM, respectively; for IN5 the EC(50) was 0.5, 13.7, and 5.7nM, respectively

Identification of the novel KI polyomavirus in paranasal and lung tissues

KI is a novel polyomavirus identified in the respiratory secretions of children with acute respiratory symptoms. Whether this reflects a causal role of the virus in the human respiratory disease remains to be established. To investigate the presence of KIV in the respiratory tissue, we examined 20 fresh lung cancer specimens and surrounding normal tissue along with one paranasal and one lung biopsy from two transplanted children. KIV-VP1 gene was detected in 9/20 lung cancer patients and 2/2 transplanted patients. However, amplification of the sequence coding for the C-terminal part of the early region of KIV performed on the 11 positive cases was successful only in two malignant lung tissues, one surrounding normal tissue, and 1/2 biopsies tested. Phylogenetic analysis performed on the early region of KIV (including the four Italian isolates), BKV and JCV revealed the presence of three distinct clades. Within the KIV clade two sub-clades were observed. A sub-clade A containing the four Italian strains, and a sub-clade B comprising the Swedish and Australian isolates. Interestingly, the two Italian strains identified in normal tissue clustered together, whereas those detected in malignant tissue fell outside this cluster. In vitro studies are needed to investigate the transforming potential of KIV strains. J. Med. Virol. 81:558-561,2009. (C) 2009 Wiley-Liss, Inc

Mechanisms underlying activity of antiretroviral drugs in HIV-1-infected macrophages: New therapeutic strategies

Monocyte-derived macrophages (M/M) are considered the second cellular target of HIV-1 and a crucial virus reservoir. M/M are widely distributed in all tissues and organs, including the CNS, where they represent the most common HIV-infected cells. Differently from activated CD4+ T lymphocytes, M/M are resistant to the cytopathic effect of HIV and survive HIV infection for a long lime. Moreover, HIV-1 replication in M/M is a key pathogenetic event during the course of HIV-1 infection. Overall findings strongly support the clinical relevance of anti-HIV drugs in M/M. Nucleoside RT inhibitors (NRTIs) are more active against HIV in M/M than in CD4+ T lymphocytes. Their activity is further boosted by the presence of an additional monophosphate group (i.e., a phosphonate group, as in the case of Tenofovir), thus overcoming the bottleneck of the low phosphorylation ability of M/M. In contrast, the antiviral activity of non-NRTIs (not affecting the DNA chain elongation) in M/M is similar to that in CD4+ T lymphocytes. Protease inhibitors are the only clinically approved drugs acting at a late stage of the HIV lifecycle. They are able to interfere with HIV replication in HIV-1 chronically infected M/M, even if at concentrations greater than those observed in HIV-1 chronically infected CD4+ T lymphocytes. Finally, several new drugs have been shown to interfere efficiently with HIV replication in M/M, including entry inhibitors. A better understanding of the activity of the anti-HIV drugs in M/M may represent a key element for the design of effective anti-HIV chemotherapy. © Society for Leukocyte Biology