14,879 research outputs found
Rank-based linkage I: triplet comparisons and oriented simplicial complexes
Rank-based linkage is a new tool for summarizing a collection of objects
according to their relationships. These objects are not mapped to vectors, and
``similarity'' between objects need be neither numerical nor symmetrical. All
an object needs to do is rank nearby objects by similarity to itself, using a
Comparator which is transitive, but need not be consistent with any metric on
the whole set. Call this a ranking system on . Rank-based linkage is applied
to the -nearest neighbor digraph derived from a ranking system. Computations
occur on a 2-dimensional abstract oriented simplicial complex whose faces are
among the points, edges, and triangles of the line graph of the undirected
-nearest neighbor graph on . In steps it builds an
edge-weighted linkage graph where
is called the in-sway between objects and . Take to be
the links whose in-sway is at least , and partition into components of
the graph , for varying . Rank-based linkage is a
functor from a category of out-ordered digraphs to a category of partitioned
sets, with the practical consequence that augmenting the set of objects in a
rank-respectful way gives a fresh clustering which does not ``rip apart`` the
previous one. The same holds for single linkage clustering in the metric space
context, but not for typical optimization-based methods. Open combinatorial
problems are presented in the last section.Comment: 37 pages, 12 figure
Likelihood Asymptotics in Nonregular Settings: A Review with Emphasis on the Likelihood Ratio
This paper reviews the most common situations where one or more regularity
conditions which underlie classical likelihood-based parametric inference fail.
We identify three main classes of problems: boundary problems, indeterminate
parameter problems -- which include non-identifiable parameters and singular
information matrices -- and change-point problems. The review focuses on the
large-sample properties of the likelihood ratio statistic. We emphasize
analytical solutions and acknowledge software implementations where available.
We furthermore give summary insight about the possible tools to derivate the
key results. Other approaches to hypothesis testing and connections to
estimation are listed in the annotated bibliography of the Supplementary
Material
Corporate Social Responsibility: the institutionalization of ESG
Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective
Minimum income support systems as elements of crisis resilience in Europe: Final Report
Mindestsicherungssysteme dienen in den meisten entwickelten Wohlfahrtsstaaten als Sicherheitsnetz letzter Instanz. Dementsprechend spielen sie gerade in wirtschaftlichen Krisenzeiten eine besondere Rolle. Inwieweit Mindestsicherungssysteme in Zeiten der Krise beansprucht werden, hängt auch von der Ausprägung vorgelagerter Sozialschutzsysteme ab. Diese Studie untersucht die Bedeutung von Systemen der Mindestsicherung sowie vorgelagerter Systeme wie Arbeitslosenversicherung, Kurzarbeit und arbeitsrechtlichem Bestandsschutz für die Krisenfestigkeit in Europa. Im Kontext der Finanzkrise von 2008/2009 und der Corona-Krise wird die Fähigkeit sozialpolitischer Maßnahmen untersucht, Armut und Einkommensverluste einzudämmen und gesellschaftliche Ausgrenzung zu vermeiden. Die Studie setzt dabei auf quantitative und qualitative Methoden, etwa multivariate Analysen, Mikrosimulationsmethoden sowie eingehende Fallstudien der Länder Dänemark, Frankreich, Irland, Polen und Spanien, die für unterschiedliche Typen von Wohlfahrtsstaaten stehen.The aim of this study is to analyse the role of social policies in different European welfare states regarding minimum income protection and active inclusion. The core focus lies on crisis resilience, i.e. the capacity of social policy arrangements to contain poverty and inequality and avoid exclusion before, during and after periods of economic shocks. To achieve this goal, the study expands its analytical focus to include other tiers of social protection, in particular upstream systems such as unemployment insurance, job retention and employment protection, as they play an additional and potentially prominent role in providing income and job protection in situations of crisis. A mixed-method approach is used that combines quantitative and qualitative research, such as descriptive and multivariate quantitative analyses, microsimulation methods and in-depth case studies. The study finds consistent differences in terms of crisis resilience across countries and welfare state types. In general, Nordic and Continental European welfare states with strong upstream systems and minimum income support (MIS) show better outcomes in core socio-economic outcomes such as poverty and exclusion risks. However, labour market integration shows some dualisms in Continental Europe. The study shows that MIS holds particular importance if there are gaps in upstream systems or cases of severe and lasting crises
Qluster: An easy-to-implement generic workflow for robust clustering of health data
The exploration of heath data by clustering algorithms allows to better describe the populations of interest by seeking the sub-profiles that compose it. This therefore reinforces medical knowledge, whether it is about a disease or a targeted population in real life. Nevertheless, contrary to the so-called conventional biostatistical methods where numerous guidelines exist, the standardization of data science approaches in clinical research remains a little discussed subject. This results in a significant variability in the execution of data science projects, whether in terms of algorithms used, reliability and credibility of the designed approach. Taking the path of parsimonious and judicious choice of both algorithms and implementations at each stage, this article proposes Qluster, a practical workflow for performing clustering tasks. Indeed, this workflow makes a compromise between (1) genericity of applications (e.g. usable on small or big data, on continuous, categorical or mixed variables, on database of high-dimensionality or not), (2) ease of implementation (need for few packages, few algorithms, few parameters, ...), and (3) robustness (e.g. use of proven algorithms and robust packages, evaluation of the stability of clusters, management of noise and multicollinearity). This workflow can be easily automated and/or routinely applied on a wide range of clustering projects. It can be useful both for data scientists with little experience in the field to make data clustering easier and more robust, and for more experienced data scientists who are looking for a straightforward and reliable solution to routinely perform preliminary data mining. A synthesis of the literature on data clustering as well as the scientific rationale supporting the proposed workflow is also provided. Finally, a detailed application of the workflow on a concrete use case is provided, along with a practical discussion for data scientists. An implementation on the Dataiku platform is available upon request to the authors
Stability for the Surface Diffusion Flow
We study the global existence and stability of surface diffusion flow (the
normal velocity is given by the Laplacian of the mean curvature) of smooth
boundaries of subsets of the --dimensional flat torus. More precisely, we
show that if a smooth set is ``close enough'' to a strictly stable critical set
for the Area functional under a volume constraint, then the surface diffusion
flow of its boundary hypersurface exists for all time and asymptotically
converges to the boundary of a ``translated'' of the critical set. This result
was obtained in dimension by Acerbi, Fusco, Julin and Morini (extending
previous results for spheres of Escher, Mayer and Simonett and Elliott and
Garcke in dimension ). Our work generalizes such conclusion to any
dimension . For sake of clarity, we show all the details in
dimension and we list the necessary modifications to the quantities
involved in the proof in the general --dimensional case, in the last
section
Multi-Attribute Utility Preference Robust Optimization: A Continuous Piecewise Linear Approximation Approach
In this paper, we consider a multi-attribute decision making problem where
the decision maker's (DM's) objective is to maximize the expected utility of
outcomes but the true utility function which captures the DM's risk preference
is ambiguous. We propose a maximin multi-attribute utility preference robust
optimization (UPRO) model where the optimal decision is based on the worst-case
utility function in an ambiguity set of plausible utility functions constructed
using partially available information such as the DM's specific preferences
between some lotteries. Specifically, we consider a UPRO model with two
attributes, where the DM's risk attitude is multivariate risk-averse and the
ambiguity set is defined by a linear system of inequalities represented by the
Lebesgue-Stieltjes (LS) integrals of the DM's utility functions. To solve the
maximin problem, we propose an explicit piecewise linear approximation (EPLA)
scheme to approximate the DM's true unknown utility so that the inner
minimization problem reduces to a linear program, and we solve the approximate
maximin problem by a derivative-free (Dfree) method. Moreover, by introducing
binary variables to locate the position of the reward function in a family of
simplices, we propose an implicit piecewise linear approximation (IPLA)
representation of the approximate UPRO and solve it using the Dfree method.
Such IPLA technique prompts us to reformulate the approximate UPRO as a single
mixed-integer program (MIP) and extend the tractability of the approximate UPRO
to the multi-attribute case. Furthermore, we extend the model to the expected
utility maximization problem with expected utility constraints where the
worst-case utility functions in the objective and constraints are considered
simultaneously. Finally, we report the numerical results about performances of
the proposed models.Comment: 50 pages,18 figure
Vegetation responses to variations in climate: A combined ordinary differential equation and sequential Monte Carlo estimation approach
Vegetation responses to variation in climate are a current research priority in the context of accelerated shifts generated by climate change. However, the interactions between environmental and biological factors still represent one of the largest uncertainties in projections of future scenarios, since the relationship between drivers and ecosystem responses has a complex and nonlinear nature. We aimed to develop a model to study the vegetation’s primary productivity dynamic response to temporal variations in climatic conditions as measured by rainfall, temperature and radiation. Thus, we propose a new way to estimate the vegetation response to climate via a non-autonomous version of a classical growth curve, with a time-varying growth rate and carrying capacity parameters according to climate variables. With a Sequential Monte Carlo Estimation to account for complexities in the climate-vegetation relationship to minimize the number of parameters. The model was applied to six key sites identified in a previous study, consisting of different arid and semiarid rangelands from North Patagonia, Argentina. For each site, we selected the time series of MODIS NDVI, and climate data from ERA5 Copernicus hourly reanalysis from 2000 to 2021. After calculating the time series of the a posteriori distribution of parameters, we analyzed the explained capacity of the model in terms of the linear coefficient of determination and
the parameters distribution variation. Results showed that most rangelands recorded changes in their sensitivity over time to climatic factors, but vegetation responses were heterogeneous and influenced by different drivers. Differences in this climate-vegetation relationship were recorded among different cases: (1) a marginal and decreasing sensitivity to temperature and radiation, respectively, but a high sensitivity to water availability; (2) high and increasing sensitivity to temperature and water availability, respectively; and (3) a case with an abrupt shift in vegetation dynamics driven by a progressively decreasing sensitivity to water availability, without any
changes in the sensitivity either to temperature or radiation. Finally, we also found that the time scale, in which the ecosystem integrated the rainfall phenomenon in terms of the width of the window function used to convolve the rainfall series into a water availability variable, was also variable in time. This approach allows us to estimate the connection degree between ecosystem productivity and climatic variables. The capacity of the model to identify changes over time in the vegetation-climate relationship might inform decision-makers about ecological transitions and the differential impact of climatic drivers on ecosystems.Estación Experimental Agropecuaria BarilocheFil: Bruzzone, Octavio Augusto. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche; ArgentinaFil: Bruzzone, Octavio Augusto. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Perri, Daiana Vanesa. Instituto Nacional de Tecnologia Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Área de Recursos Naturales; ArgentinaFil: Perri, Daiana Vanesa. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; ArgentinaFil: Easdale, Marcos Horacio. Instituto Nacional de Tecnologia Agropecuaria (INTA). Estación Experimental Agropecuaria Bariloche. Área de Recursos Naturales; ArgentinaFil: Easdale, Marcos Horacio. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Instituto de Investigaciones Forestales y Agropecuarias Bariloche; Argentin
Word maps with constants on symmetric groups
We study word maps with constants on symmetric groups. Even though there are
mixed identities of bounded length that are valid for all symmetric groups, we
show that no such identities hold in a metric sense. Moreover, we prove that
word maps with constants and non-trivial content that are short enough have an
image of positive diameter only depending on the length of the word. Finally,
we also show that every self-map on a finite non-abelian simple group
is actually a word map with constants from .Comment: 13 pages, no figure
Mixed volumes of networks with binomial steady-states
The steady-state degree of a chemical reaction network is the number of
complex steady-states for generic rate constants and initial conditions. One
way to bound the steady-state degree is through the mixed volume of the
steady-state system or an equivalent system. In this work, we show that for
partionable binomial networks, whose resulting steady-state systems are given
by a set of binomials and a set of linear (not necessarily binomial)
conservation equations, computing the mixed volume is equivalent to finding the
volume of a single mixed cell that is the translate of a parallelotope. We then
turn our attention to identifying cycles with binomial steady-state ideals. To
this end, we give a coloring condition on directed cycles that guarantees the
network has a binomial steady-state ideal. We highlight both of these theorems
using a class of networks referred to as species-overlapping networks and give
a formula for the mixed volume of these networks.Comment: 17 page
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