Paola Annoni

Bio: Paola Annoni is an academic researcher from University of Milan. The author has contributed to research in topics: European union & Variance-based sensitivity analysis. The author has an hindex of 13, co-authored 40 publications receiving 3188 citations. Previous affiliations of Paola Annoni include Institute for the Protection and Security of the Citizen & International Practical Shooting Confederation.

How to avoid a perfunctory sensitivity analysis

Abstract: Mathematical modelers from different disciplines and regulatory agencies worldwide agree on the importance of a careful sensitivity analysis (SA) of model-based inference. The most popular SA practice seen in the literature is that of 'one-factor-at-a-time' (OAT). This consists of analyzing the effect of varying one model input factor at a time while keeping all other fixed. While the shortcomings of OAT are known from the statistical literature, its widespread use among modelers raises concern on the quality of the associated sensitivity analyses. The present paper introduces a novel geometric proof of the inefficiency of OAT, with the purpose of providing the modeling community with a convincing and possibly definitive argument against OAT. Alternatives to OAT are indicated which are based on statistical theory, drawing from experimental design, regression analysis and sensitivity analysis proper.

Non-parametric methods for global sensitivity analysis of model output with dependent inputs

Abstract: This paper addresses the issue of performing global sensitivity analysis of model output with dependent inputs. First, we define variance-based sensitivity indices that allow for distinguishing the independent contributions of the inputs to the response variance from their mutual dependent contributions. Then, two sampling strategies are proposed for their non-parametric, numerical estimation. This approach allows us to estimate the sensitivity indices not only for individual inputs but also for groups of inputs. After testing the accuracy of the non-parametric method on some analytical test functions, the approach is employed to assess the importance of dependent inputs on a computer model for the migration of radioactive substances in the geosphere. We define a set of variance-based sensitivity indices for models with dependent inputs.The new sensitivity indices are those of the Rosenblatt transforms of the original variables.Non-parametric sampling-based strategies are proposed to compute the sensitivity indices.When input dependency is simply correlation, the simpler sampling procedure by Iman and Conover can be used.The proposed indices are computed and discussed for a radionuclide transport model a benchmark in sensitivity analysis.

Measuring quality of government in EU regions across space and time

Abstract: A wave of recent cross‐national research has pointed to the positive consequences for countries with high levels of “quality of government” (QoG), broadly defined, such as corruption, impartiality, and quality of public services. Yet the question of how QoG varies at the sub‐national level is still widely overlooked, in particular with measures that are available over time. To address it, we present the third round of data from the regional European Quality of Government Index (EQI) survey corruption (D73), Europe (N44) governance (H11); sub‐national (R50), time series (C22), collected in 2017 and built upon the opinions of 78,000 respondents in 193 regions from 21 European countries. The data provides several contributions to the literature. First, while the majority of QoG‐type indices rely on expert assessments, the EQI relies on the assessments of citizens, who are the on‐the‐ground consumers of public services. Second, the data begins to show trends on QoG variation over time, as well as across European regions. Consequently, this data is the most comprehensive sub‐national data to date; mapping of QoG within and across EU countries over the past decade. Building on previous rounds of data collected in 2010 and 2013, the 2017 EQI, which is published free for scholarly use, builds on both perceptions and experiences of citizens in public service areas such as health care, education, and law enforcement. This paper presents the results of the latest survey, improved with respect to the previous ones, discussion of trends across space and over time, as well as interesting avenues for future research that we detect across European regions. Una serie de investigaciones internacionales recientes ha senalado las consecuencias positivas para los paises con altos niveles de “calidad de gobierno” (CdG), definida en terminos generales como corrupcion, imparcialidad y calidad de los servicios publicos. Sin embargo, la cuestion de como varia la CdG a nivel subnacional aun se suele seguir pasando por alto, en particular respecto a las medidas disponibles a lo largo del tiempo. Para abordarlo, se presenta aqui la tercera ronda de datos de la encuesta regional del Indice Europeo de Calidad de Gobierno (EQI, por sus siglas en ingles) sobre corrupcion (D73), gobernanza (H11) en Europa (N44); subnacional (R50), series temporales (C22), recopilada en 2017 y basada en las opiniones de 78 000 encuestados de 193 regiones de 21 paises europeos. Los datos proporcionan varias contribuciones a la literatura. En primer lugar, mientras que la mayoria de los indices de tipo CdG se basan en evaluaciones expertas, la EQI se basa en las evaluaciones de los ciudadanos, que son los consumidores sobre el terreno de los servicios publicos. En segundo lugar, los datos empiezan a mostrar las tendencias de la variacion de la CdG a lo largo del tiempo, asi como en todas las regiones europeas. Por consiguiente, esta encuesta ofrece los datos subnacionales mas completos hasta la fecha y el cartografiado de la CdG en los paises de la UE y entre paises ,durante la ultima decada. A partir de las rondas anteriores de datos recopilados en 2010 y 2013, la EQI 2017, que se publica gratuitamente para uso academico, se basa tanto en las percepciones como en las experiencias de los ciudadanos en diferentes areas de servicio publico como la atencion de la salud, la educacion y la aplicacion de la ley. Este documento presenta los resultados de la ultima encuesta, mejorada con respecto a las anteriores, un debate sobre las tendencias espaciales y temporales, asi como vias interesantes para la investigacion futura que detectamos en las regiones europeas. 政府の質(quality of government:QoG)は、腐敗、公正性、公共サービスの質など定義は広いが、多数の国際研究が、QoGのレベルの高い国のプラスの結果を相次いで指摘している。地方レベルではQoGがどのように異なるかという疑問は、予てからその解決方法は利用可能であるにもかかわらず、未だに広く見過されている。そこで、2017年に収集された、欧州21カ国における193地域の78,000人の回答者の意見に基づいて構築された、地域のEuropean Quality of Government Index(EQI:欧州における政府の質指数)による調査腐敗(D73)、欧州(N44)統治(H11)、地方レベル(C22)、時系列(R50)からの第三のデータを提示した。データから以下の知見が得られた。1)QoGの分類指標の多くが専門家の評価に依存するものであるが、EQIは、公共サービスの実質的利用者である一般市民の評価に依存している。2)データは、QoGの変化のトレンドを経時的に、かつ欧州全域的にも示すようになってきている。すなわち、このデータは現時点で最も包括的な地方データであり、過去10年におけるEU圏内全域のQoGの分布を示すものである。2017年のEQIは、2010年と2013年に収集されたEQIの既存データに基づいて構築され、これは研究用に無料で公開されているが、医療、教育、法執行機関などの公共サービス領域に従事する市民の理解と経験の両方に基づいて構築されている。本稿では、過去の調査と比較して改善された、最新の調査の結果を提示し、空間と時間を超えたトレンドの考察と、欧州全域で認められた将来の研究に向かう興味深い道を提示する。

Reliability Engineering and System Safety

Abstract: This paper presents a polynomial dimensional decomposition (PDD) method for global sensitivity analysis of stochastic systems subject to independent random input following arbitrary probability distributions. The method involves Fourier-polynomial expansions of lower-variate component functions of a stochastic response by measure-consistent orthonormal polynomial bases, analytical formulae for calculating the global sensitivity indices in terms of the expansion coefficients, and dimension-reduction integration for estimating the expansion coefficients. Due to identical dimensional structures of PDD and analysis-of-variance decomposition, the proposed method facilitates simple and direct calculation of the global sensitivity indices. Numerical results of the global sensitivity indices computed for smooth systems reveal significantly higher convergence rates of the PDD approximation than those from existing methods, including polynomial chaos expansion, random balance design, state-dependent parameter, improved Sobol’s method, and sampling-based methods. However, for non-smooth functions, the convergence properties of the PDD solution deteriorate to a great extent, warranting further improvements. The computational complexity of the PDD method is polynomial, as opposed to exponential, thereby alleviating the curse of dimensionality to some extent. Mathematical modeling of complex systems often requires sensitivity analysis to determine how an output variable of interest is influenced by individual or subsets of input variables. A traditional local sensitivity analysis entails gradients or derivatives, often invoked in design optimization, describing changes in the model response due to the local variation of input. Depending on the model output, obtaining gradients or derivatives, if they exist, can be simple or difficult. In contrast, a global sensitivity analysis (GSA), increasingly becoming mainstream, characterizes how the global variation of input, due to its uncertainty, impacts the overall uncertain behavior of the model. In other words, GSA constitutes the study of how the output uncertainty from a mathematical model is divvied up, qualitatively or quantitatively, to distinct sources of input variation in the model [1].

Position paper: Characterising performance of environmental models

Abstract: In order to use environmental models effectively for management and decision-making, it is vital to establish an appropriate level of confidence in their performance. This paper reviews techniques available across various fields for characterising the performance of environmental models with focus on numerical, graphical and qualitative methods. General classes of direct value comparison, coupling real and modelled values, preserving data patterns, indirect metrics based on parameter values, and data transformations are discussed. In practice environmental modelling requires the use and implementation of workflows that combine several methods, tailored to the model purpose and dependent upon the data and information available. A five-step procedure for performance evaluation of models is suggested, with the key elements including: (i) (re)assessment of the model's aim, scale and scope; (ii) characterisation of the data for calibration and testing; (iii) visual and other analysis to detect under- or non-modelled behaviour and to gain an overview of overall performance; (iv) selection of basic performance criteria; and (v) consideration of more advanced methods to handle problems such as systematic divergence between modelled and observed values.

Sensitivity analysis of environmental models

Abstract: Sensitivity Analysis (SA) investigates how the variation in the output of a numerical model can be attributed to variations of its input factors. SA is increasingly being used in environmental modelling for a variety of purposes, including uncertainty assessment, model calibration and diagnostic evaluation, dominant control analysis and robust decision-making. In this paper we review the SA literature with the goal of providing: (i) a comprehensive view of SA approaches also in relation to other methodologies for model identification and application; (ii) a systematic classification of the most commonly used SA methods; (iii) practical guidelines for the application of SA. The paper aims at delivering an introduction to SA for non-specialist readers, as well as practical advice with best practice examples from the literature; and at stimulating the discussion within the community of SA developers and users regarding the setting of good practices and on defining priorities for future research. We present an overview of SA and its link to uncertainty analysis, model calibration and evaluation, robust decision-making.We provide a systematic review of existing approaches, which can support users in the choice of an SA method.We provide practical guidelines by developing a workflow for the application of SA and discuss critical choices.We give best practice examples from the literature and highlight trends and gaps for future research.