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The identification of asymmetric conditional heteroscedasticity is often based on sample cross-correlations between past and squared observations. In this paper we analyse the effects of outliers on these cross-correlations and, consequently, on the identification of asymmetric volatilities. We show that, as expected, one isolated big outlier biases the sample cross-correlations towards zero and hence could hide true leverage effect. Unlike, the presence of two or more big consecutive outliers could lead to detecting spurious asymmetries or asymmetries of the wrong sign. We also address the problem of robust estimation of the cross-correlations by extending some popular robust estimators of pairwise correlations and autocorrelations. Their finite sample resistance against outliers is compared through Monte Carlo experiments. Situations with isolated and patchy outliers of different sizes are examined. It is shown that a modified Ramsay-weighted estimator of the cross-correlations outperforms other estimators in identifying asymmetric conditionally heteroscedastic models. Finally, the results are illustrated with an empirical application.

We conduct a field experiment to show that discrimination in the rental market represents a significant obstacle for the residential mobility of immigrants and contributes to the ethnic residential segregation observed in large cities. We employ the Internet platform to identify vacant rental apartments in different areas of the two largest Spanish cities, Madrid and Barcelona. We send emails showing interest in the apartments and signal the applicants’ ethnicity by using native and foreign-sounding names. We find that, in line with previous studies, immigrants face a differential treatment when trying to rent an apartment. Our results also indicate that this negative treatment varies considerably with the share of immigrants in the area. In neighborhoods with a scarce presence of immigrants the response rate is 30 percentage points lower for immigrants than for natives, while this differential decays towards zero as the immigration share increases. This evidence indicates that discriminatory practices may perpetuate the spatial segregation of minority groups.

PurposeTo identify risk factors present at admission in adult patients hospitalised due to influenza virus infection during the 2009/10 and 2010/11 seasons—including whether infection was from pandemic or seasonal influenza A infections—that were associated with the likelihood of developing severe pneumonia with multilobar involvement and shock.MethodsProspective cohort study. Patients hospitalised due to influenza virus infection were recruited. We collected information on sociodemographic characteristics, pre-existing medical conditions, vaccinations, toxic habits, previous medications, exposure to social environments, and EuroQoL-5D (EQ-5D). Severe pneumonia with multilobar involvement and/or shock (SPAS) was the primary outcome of interest. We constructed two multivariate logistic regression models to explain the likelihood of developing SPAS and to create a clinical prediction rule for developing SPAS that includes clinically relevant variables.ResultsLaboratory-confirmed A(H1N1)pdm09, EQ-5D utility score 7 days before admission, more than one comorbidity, altered mental status, dyspnoea on arrival, days from onset of symptoms, and influenza season were associated with SPAS. In addition, not being vaccinated against seasonal influenza in the previous year, anaemia, altered mental status, fever and dyspnoea on arrival at hospital, difficulties in performing activities of daily living in the previous 7 days, and days from onset of symptoms to arrival at hospital were related to the likelihood of SPAS (area under the curve value of 0.75; Hosmer–Lemeshow p value of 0.84).ConclusionsThese variables should be taken into account by physicians evaluating a patient affected by influenza as additional information to that provided by the usual risk scores.

The setting up of a condition monitoring program is considered a strategic decision owing to its influence on cost, safety, quality, and availability in an industrial plant. However, no previous structured analysis of the selection of condition monitoring techniques and their characteristics has been made, so further analysis is needed. This paper proposes a model for the selection of the most suitable condition monitoring technique to be set up in a subsidiary of a petrochemical plant. For this purpose, different criteria, indicators, and utility functions are defined; nine alternatives are proposed depending on the technological level of the condition monitoring program. The model applies the analytic hierarchy process to obtain a ranking of alternatives. Owing to the special characteristics of the decision, discrete probability distributions are defined. The final ranking is computed by applying the Monte Carlo method considering 2000 simulation trials. Finally, a sensitivity analysis is applied.

In this work, a procedure for solving the optimal design and upgrade of linear sensor networks, subject to quality constraints on a set of key variable estimates, is presented. The strategy aims to select the optimal set of flowmeters without imposing restrictions on the mathematical nature of the objective function and constraints. An evolutionary technique based on genetic algorithms (GAs) is proposed that combines the benefits of using structured populations in the form of neighborhoods and a local search strategy. Both procedures take advantage of existing process knowledge. Application examples are provided for the instrumentation design of a steam metering network of a methanol plant, which show that the algorithm has a good balance among its exploration and exploitation capabilities.

In this work, solution strategies for the optimal design of nonredundant observable linear sensor networks are discussed. The Greedy algorithm allows the problem only to be tackled for a subset of optimization criteria. Particular deterministic techniques or general evolutionary strategies are necessary to solve the problem for more complex objective functions. In this context, a procedure based on the application of genetic algorithms (GAs) and linear algebra is presented. Ad hoc operators are designed for the crossover and mutation operations because the classic genetic operators perform poorly. In contrast to ad hoc deterministic codes, which find the design solution for each specific criteria, this strategy allows the problem to be solved with different objective functions using the same implementation. Furthermore, this code is extended to handle multiobjective problems through a modification of only the selection operator. An industrial example is provided to show the efficiency of the algorithm.

Summary The VP73 structural protein is a major viral protein in MS cells infected with African swine fever virus. It can be purified by simple extraction from the cytoplasmic fraction of infected cells and used as an effective antigen in ELISA studies.

Restriction enzyme cleavage maps for the fragments produced by Cla I, Sal I and Sma I have been constructed for African swine fever virus (ASFV) DNA grown in pig leukocytes (strain E70 L6) and after adaptation to growth in MS monkey kidney cells (strain E70MS14). The mapping data revealed that before adaptation to growth in MS cells, the size of the DNA from ASFV strain E70 L6 was l73 Kbp and after adaptation it was only 156 Kbp. The decrease in size was produced by deletions and additions mainly in the terminal regions of the genome. These genetic variations were located between 0.0 to 0.01 m.u. (Cla I-M1 fragment), 0.04 to 0.14 m.u. (Sma I-B1, Sal I-A1 fragments), 0.51 to 0.52 m.u. (Cla I-O fragment), 0.84 to 0.86 m.u. (Sma I-H1), 0.95 to 0.97 m.u. (Cla I-A1, Cla I-G1 fragments) and 0.99 to 1.0 m.u. (Cla I-G1) on viral genome of ASFV grown in pig leukocytes.

Summary Viral DNA subpopulations were produced when the ASFV was grown in monkey kidney MS cells. They were detected after 44 passages but not during the first 14 passages or in the unadapted ASFV E 70 strain grown in pig leukocytes. Different viral variants were isolated and their genomes were characterized. Restriction enzyme site variations were detected in both terminal fragments, Cla I-M and Sal I-F, and in the internal fragments Clal-O and Sma I-H. These variations result in changes in the size of the viral genome which ranges from 156 Kbp to 170 Kbp.

In the present study, we analyzed the cell cycle distribution of bone marrow (BM) cells in 120 untreated multiple myeloma patients using a DNA/CD38 double-staining technique at flow cytometry in which plasma cells (PCs) can be clearly discriminated from residual BM cells based on their CD38 expression. This approach allows us to determine the proliferative activity of both PCs and residual normal BM cells. The percentage of S-phase cells in the myelomatous population was found to be significantly lower than that of the residual normal BM cells (P < .001). Regarding the proliferative activity of myelomatous cells, patients with a high number of S-phase PCs (> 3%) showed a significantly (P < .05) increased incidence of anemia and hypercalcemia; higher values of beta 2-microglobulin (beta 2M), urea, and creatinine; and higher numbers of peripheral blood natural killer cells, as well as a poor prognosis as assessed both by response duration and overall survival. With respect to the residual BM normal fraction, a low proliferative activity was significantly (P < .05) associated with the presence of anemia and neutropenia together with increased numbers of BM PCs, a higher incidence of Bence Jones myelomas, and DNA diploidy. Multivariate analysis showed that the number of S-phase PCs was the most important independent prognostic factor, allowing us to discriminate two subgroups of patients with different prognoses, even within the same clinical stage. Moreover, the S-phase PCs, together with beta 2M, age, and performance status, represent the best combination of disease characteristics for stratifying patients according to prognosis and allow the establishment of a simple and powerful staging system for multiple myeloma patients. In addition, this classification can be used for planning treatment in patients who are candidates for transplantation.

In the present study, we analyzed the cell cycle distribution of bone marrow (BM) cells in 120 untreated multiple myeloma patients using a DNA/CD38 double-staining technique at flow cytometry in which plasma cells (PCs) can be clearly discriminated from residual BM cells based on their CD38 expression. This approach allows us to determine the proliferative activity of both PCs and residual normal BM cells. The percentage of S-phase cells in the myelomatous population was found to be significantly lower than that of the residual normal BM cells (P< .001). Regarding the proliferative activity of myelomatous cells, patients with a high number of S-phase PCs (>3%) showed a significantly (P< .05) increased incidence of anemia and hypercalcemia; higher values of β2-microglobulin (β2M), urea, and creatinine; and higher numbers of peripheral blood natural killer cells, as well as a poor prognosis as assessed both by response duration and overall survival. With respect to the residual BM normal fraction, a low proliferative activity was significantly (P< .05) associated with the presence of anemia and neutropenia together with increased numbers of BM PCs, a higher incidence of Bence Jones myelomas, and DNA diploidy. Multivariate analysis showed that the number of S-phase PCs was the most important independent prognostic factor, allowing us to discriminate two subgroups of patients with different prognoses, even within the same clinical stage. Moreover, the S-phase PCs, together with β2M, age, and performance status, represent the best combination of disease characteristics for stratifying patients according to prognosis and allow the establishment of a simple and powerful staging system for multiple myeloma patients. In addition, this classification can be used for planning treatment in patients who are candidates for transplantation.

This article shows that the relationship between kurtosis, persistence of shocks to volatility, and first-order autocorrelation of squares is different in GARCH and ARSV models. This difference can explain why, when these models are fitted to the same series, the persistence estimated is usually higher in GARCH than in ARSV models, and, why gaussian ARSV models seem to be adequate, whereas GARCH models often require leptokurtic conditional distributions. We also show that introducing the asymmetric response of volatility to positive and negative returns does not change the conclusions. These results are illustrated with the analysis of daily financial returns.