Mallows and generalized Mallows model for matchings

Irurozki, Ekhiñe; Calvo, B.; Lozano, José A.

doi:10.3150/17-bej1017

Cited by 15 publications

(23 citation statements)

References 36 publications

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“…In addition to adjusted R 2 , when the goal is to find the most appropriate model involving multitude subsets of regressors, a criterion of Mallows' C p statistic (Equation (2)) is generally applied [60,61]. Examples include checking matchings between the subsets [108], model averaging [109][110][111], measuring the deviations from perfect rankings [112], and model selection [113].…”

Section: Stagementioning

confidence: 99%

Capturing a Complexity of Nutritional, Environmental, and Economic Impacts on Selected Health Parameters in the Russian High North

Tianming

Erokhin

2020

Sustainability

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The rapid pace of economic exploration of the Arctic against the backdrop of progressing environmental change put a high priority on improving understanding of health impacts in the northern communities. Deficiencies in the capability to capture the complexity of health-influencing parameters along with a lack of observations in circumpolar territories present major challenges to establishing credible projections of disease incidence across varying northern environments. It is thus crucial to reveal the relative contributions of coacting factors to provide a basis for sustainable solutions in the sphere of public health. In order to better understand the adverse effects associated with public health, this study employed six-stage multiple regression analysis of incidence rates of fourteen diseases (International Classification of Diseases (ICD-11) codes most widespread in the Russian Arctic) against a set of environmental, nutritional, and economic variables. Variance inflationary factor and best-subsets regression methods were used to eliminate collinearity between the parameters of regression models. To address the diversity of health impacts across northern environments, territories of the Arctic zone of Russia were categorized as (1) industrial sites, (2) urban agglomerations, (3) rural inland, and (4) coastline territories. It was suggested that, in Type 1 territories, public health parameters were most negatively affected by air and water pollution, in Type 2 territories—by low-nutrient diets, in Type 3 and Type 4 territories—by economic factors. It was found that in the Western parts of the Russian Arctic, poor quality of running water along with low access to the quality-assured sources of water might increase the exposure to infectious and parasitic diseases and diseases of the circulatory, respiratory, and genitourinary systems. Low living standards across the Russian Arctic challenged the economic accessibility of adequate diets. In the cities, the nutritional transition to low-quality cheap market food correlated with a higher incidence of digestive system disorders, immune diseases, and neoplasms. In indigenous communities, the prevalence of low diversified diets based on traditional food correlated with the increase in the incidence rates of nutritional and metabolic diseases.

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Section: Stagementioning

confidence: 99%

Capturing a Complexity of Nutritional, Environmental, and Economic Impacts on Selected Health Parameters in the Russian High North

Tianming

Erokhin

2020

Sustainability

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“…The Mallows Models (MM) is a family of probabilistic models on the space of permutations of size n (S n ). The MM is one of the most simple and natural distributions on S n , requiring just two parameters: the concentration parameter θ ∈ R + and the location parameter, σ 0 ∈ S n [5]. The location parameter, also known as the central permutation, is the mode of the distribution.…”

Section: Distance Based Probability Modelsmentioning

confidence: 99%

“…Roughly, the sampling procedure is as follows: We center an MM in a permutation chosen u.a.r from the selected set of permutations (where θ is set according to the exploration-exploitation scheme) and sample a new permutation from this distribution. In particular, we use the Distance Sampling Algorithm, see [5] for details and implementations. As stated previously, our algorithm hybridizes an EDA with a bestfirst local search procedure.…”

Section: Hamming Based Kmm Edamentioning

confidence: 99%

Approaching the quadratic assignment problem with kernels of mallows models under the hamming distance

Arza

Ceberio

Pérez

et al. 2019

Proceedings of the Genetic and Evolutionary Computation Conference Companion

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The Quadratic Assignment Problem (QAP) is a specially challenging permutation-based np-hard combinatorial optimization problem, since instances of size n > 40 are seldom solved using exact methods. In this sense, many approximate methods have been published to tackle this problem, including Estimation of Distribution Algorithms (EDAs). In particular, EDAs have been used to solve permutation problems by introducing distance based exponential models, such as the Mallows Models. In this paper we approximate the QAP with a Hamming distance based kernels of Mallows Models. Based on the benchmark instances, we have observed that our approach is competitive, reaching the best-known solution in 71% of the tested instances, especially on large instances (n > 125), where it is able to outperform state of the art results in 43 out of 288 instances.

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“…Inspired by the generalised Mallows model (Irurozki et al, 2019), the model here using the Hamming distance can be extended to one using the weighted Hamming distance. By introducing T precision parameters λ 1 , .…”

Section: Exponential-distance Modelsmentioning

confidence: 99%

Clustering Longitudinal Life-Course Sequences Using Mixtures of Exponential-Distance Models

Murphy¹,

Murphy²,

Piccarreta³

et al. 2019

Preprint

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Sequence analysis is an increasingly popular approach for the analysis of life-courses represented by categorical sequences, i.e. as the ordered collection of activities experienced by subjects over a given time period. Several criteria have been introduced in the literature to measure pairwise dissimilarities among sequences. Typically, dissimilarity matrices are employed as the input to heuristic clustering algorithms, with the aim of identifying the most relevant patterns in the data.Here, we propose a model-based clustering approach for categorical sequence data. The technique is applied to a popular survey data set containing information on the career trajectories, in terms of monthly labour market activities, of a cohort of Northern Irish youths tracked from the age of 16 to the age of 22.Specifically, we develop a family of methods for clustering sequence data directly based on mixtures of exponential-distance models, which we call MEDseq. The Hamming distance and weighted variants thereof are employed as the distance metric. The existence of a closed-form expression for the normalising constant using these metrics facilitates the development of an ECM algorithm for model fitting. We allow the probability of component membership to depend on fixed covariates. The MEDseq models can also accommodate sampling weights, which are typically associated with life-course data. Including the weights and covariates in the clustering process in a holistic manner allows new insights to be gleaned from the Northern Irish data.

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Mallows and generalized Mallows model for matchings

Cited by 15 publications

References 36 publications

Capturing a Complexity of Nutritional, Environmental, and Economic Impacts on Selected Health Parameters in the Russian High North

Capturing a Complexity of Nutritional, Environmental, and Economic Impacts on Selected Health Parameters in the Russian High North

Approaching the quadratic assignment problem with kernels of mallows models under the hamming distance

Clustering Longitudinal Life-Course Sequences Using Mixtures of Exponential-Distance Models

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