634 research outputs found
Asymptotic robustness of Kelly's GLRT and Adaptive Matched Filter detector under model misspecification
A fundamental assumption underling any Hypothesis Testing (HT) problem is
that the available data follow the parametric model assumed to derive the test
statistic. Nevertheless, a perfect match between the true and the assumed data
models cannot be achieved in many practical applications. In all these cases,
it is advisable to use a robust decision test, i.e. a test whose statistic
preserves (at least asymptotically) the same probability density function (pdf)
for a suitable set of possible input data models under the null hypothesis.
Building upon the seminal work of Kent (1982), in this paper we investigate the
impact of the model mismatch in a recurring HT problem in radar signal
processing applications: testing the mean of a set of Complex Elliptically
Symmetric (CES) distributed random vectors under a possible misspecified,
Gaussian data model. In particular, by using this general misspecified
framework, a new look to two popular detectors, the Kelly's Generalized
Likelihood Ration Test (GLRT) and the Adaptive Matched Filter (AMF), is
provided and their robustness properties investigated.Comment: ISI World Statistics Congress 2017 (ISI2017), Marrakech, Morocco,
16-21 July 201
Performance Bounds for Parameter Estimation under Misspecified Models: Fundamental findings and applications
Inferring information from a set of acquired data is the main objective of
any signal processing (SP) method. In particular, the common problem of
estimating the value of a vector of parameters from a set of noisy measurements
is at the core of a plethora of scientific and technological advances in the
last decades; for example, wireless communications, radar and sonar,
biomedicine, image processing, and seismology, just to name a few. Developing
an estimation algorithm often begins by assuming a statistical model for the
measured data, i.e. a probability density function (pdf) which if correct,
fully characterizes the behaviour of the collected data/measurements.
Experience with real data, however, often exposes the limitations of any
assumed data model since modelling errors at some level are always present.
Consequently, the true data model and the model assumed to derive the
estimation algorithm could differ. When this happens, the model is said to be
mismatched or misspecified. Therefore, understanding the possible performance
loss or regret that an estimation algorithm could experience under model
misspecification is of crucial importance for any SP practitioner. Further,
understanding the limits on the performance of any estimator subject to model
misspecification is of practical interest. Motivated by the widespread and
practical need to assess the performance of a mismatched estimator, the goal of
this paper is to help to bring attention to the main theoretical findings on
estimation theory, and in particular on lower bounds under model
misspecification, that have been published in the statistical and econometrical
literature in the last fifty years. Secondly, some applications are discussed
to illustrate the broad range of areas and problems to which this framework
extends, and consequently the numerous opportunities available for SP
researchers.Comment: To appear in the IEEE Signal Processing Magazin
Semiparametric Inference and Lower Bounds for Real Elliptically Symmetric Distributions
This paper has a twofold goal. The first aim is to provide a deeper
understanding of the family of the Real Elliptically Symmetric (RES)
distributions by investigating their intrinsic semiparametric nature. The
second aim is to derive a semiparametric lower bound for the estimation of the
parametric component of the model. The RES distributions represent a
semiparametric model where the parametric part is given by the mean vector and
by the scatter matrix while the non-parametric, infinite-dimensional, part is
represented by the density generator. Since, in practical applications, we are
often interested only in the estimation of the parametric component, the
density generator can be considered as nuisance. The first part of the paper is
dedicated to conveniently place the RES distributions in the framework of the
semiparametric group models. The second part of the paper, building on the
mathematical tools previously introduced, the Constrained Semiparametric
Cram\'{e}r-Rao Bound (CSCRB) for the estimation of the mean vector and of the
constrained scatter matrix of a RES distributed random vector is introduced.
The CSCRB provides a lower bound on the Mean Squared Error (MSE) of any robust
-estimator of mean vector and scatter matrix when no a-priori information on
the density generator is available. A closed form expression for the CSCRB is
derived. Finally, in simulations, we assess the statistical efficiency of the
Tyler's and Huber's scatter matrix -estimators with respect to the CSCRB.Comment: This paper has been accepted for publication in IEEE Transactions on
Signal Processin
The strategic role of the corporate social responsibility and circular economy in the cosmetic industry
In the literature, circular economy (CE) and corporate social responsibility (CSR) are increasingly interconnected concepts. Turon at al. (2016) consider CE the guidelines of conduct for designing and developing good CSR strategies. In particular, the corporate management philosophy needs to be translated into mandatory CSR reports that better frames circular economy objectives by identifying and communicating actions to achieve sustainable development goals. The purpose of this paper is to explore a number of CSR reports in order to understand if cosmetic multinationals' (MNC) nonfinancial reporting is focused on the concept of circular economy and if CSR reports ensure an adequate level of disclosure to circular strategies. Moreover, the paper highlights the advantages that arise by converging the concepts of CSR and CE. The originality of this paper lies on providing evidence on "how" MNC are implementing a circular model. This paper contributes to our understanding on the relation between CSR and CE; it assesses the state of the art of circular strategies in MNC and proposes a consolidation of the concept of CE in terms of sustainable strategic and managerial practices communicated to the market by CSR reports. Moreover, it brings MNC to a better understanding of the ways to communicate their new circular business model. The analysis reveals a good level of attention by MNC to circularity in drafting their CSR reports that in many cases are able to describe objectives and actions that embrace multiple dimensions
Circular economy and corporate social responsibility in the agricultural system: Cases study of the Italian agri-food industry
The persistent exploitation of natural resources and the consequent use of consumption are driving global food demand with the result that agricultural activity is becoming less and less environmentally friendly. The circular economy (CE) can become a valid alternative, inserting the economic-agricultural system into the harmonic process of material circulation. The corporate social responsibility (CSR) model is particularly interesting not only because of the ethical dimension of the company but also as a factor of strategic business improvement that combines the concepts of CSR and CE as possible solutions for developing sustainable business processes. The objective of the work is to highlight a detailed framework of how the small and medium-sized enterprises (SMEs) of the Italian agri-food industry can provide an ade-quate level of communication, circular strategies and social responsibility practices. The research methodology is based on a qualitative multiple study conducted on a sample of nine companies in the Italian territory. The study highlights the attention of companies on the issues of the CE for the achievement of the set sustainable objectives and the attention to CSR and CE practices. The work has several implications. It provides a further understanding of CSR and CE policies as enabling factors for the development of sustainable organizational performance in agriculture. Moreover, it better investigates the relationship between CSR and the CE. Finally, it analyses the SMEs state of the art in the CE field and strengthens the concept of CE by analysing corporate practices consistent with sustainability reports
Corporate social responsibility as a catalyst of circular economy? A case study perspective in Agri-food
Purpose: This study aims to analyze how, under the stakeholder theory, corporate social responsibility (CSR) might favor the emergence of circular economy (CE) in the Agri-food sector, which is a relevant context, as it is technologically dynamic and requires paying attention to all the stakeholders. Design/methodology/approach: An exploratory, qualitative research design has been adopted to study the phenomenon in detail, as it facilitates the understanding of complex phenomena such those under investigation and helps enrich existing theory with new insights from real-world cases to add theoretical generalizations to the existing body of research in the field. Findings: The results of this study highlight that companies adopting CSR models are oriented toward circularity. Practical implications: This study provides useful indications to managers and policymakers as to how to favor the two approaches (CSR and CE) and benefit all the stakeholders. Originality/value: While there is wide scholarly and managerial interest toward CSR and CE, previous research has mainly analyzed CE and CSR as two independent phenomena. Therefore, there is a lack of understanding about how the two areas are linked. Following previous studies that have started to theoretically argue an interconnection between CSR and CE, in this research, it has been empirically investigated, and further explored theoretically, whether CSR can implicitly encourage the emergence of CE approaches
Scaling up MIMO Radar for Target Detection
This work focuses on target detection in a colocated MIMO radar system. Instead of exploiting the »classical' temporal domain, we propose to explore the spatial dimension (i.e., number of antennas M) to derive asymptotic results for the detector. Specifically, we assume no a priori knowledge of the statistics of the autoregressive data generating process and propose to use a mispecified Wald-type detector, which is shown to have an asymptotic χ-squared distribution as M → ∞. Closed-form expressions for the probabilities of false alarm and detection are derived. Numerical results are used to validate the asymptotic analysis in the finite system regime. It turns out that, for the considered scenario, the asymptotic performance is closely matched already for M ≥ 50
Massive MIMO radar for target detection
Since the seminal paper by Marzetta from 2010, the Massive MIMO paradigm in communication systems has changed from being a theoretical scaled-up version of MIMO, with an infinite number of antennas, to a practical technology. Its key concepts have been adopted in the 5G new radio standard and base stations, where 64 fully-digital transceivers have been commercially deployed. Motivated by these recent developments, this paper considers a co-located MIMO radar with MT transmitting and MR receiving antennas and explores the potential benefits of having a large number of virtual spatial antenna channels N=MTMR. Particularly, we focus on the target detection problem and develop a robust Wald-type test that guarantees certain detection performance, regardless of the unknown statistical characterization of the disturbance. Closed-form expressions for the probabilities of false alarm and detection are derived for the asymptotic regime N→∞. Numerical results are used to validate the asymptotic analysis in the finite system regime with different disturbance models. Our results imply that there always exists a sufficient number of antennas for which the performance requirements are satisfied, without any a-priori knowledge of the disturbance statistics. This is referred to as the Massive MIMO regime of the radar system
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