Robust estimation on a parametric model via testing

Sart, Mathieu

doi:10.3150/15-bej706

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…This result shows that when the model is regular enough and contains the true density, ρ-estimation allows to recover the MLE, at least when n is large enough. The numerical study of Mathieu Sart (2016) on very simple statistical models S seems to indicate that our procedure allows to recover the MLE in almost all simulations even when the number of observations n is small. Consequently, there seems to be some space for improvement in Theorem 19.…”

Section: Connection With the Maximum Likelihood Estimatormentioning

confidence: 83%

A new method for estimation and model selection: $$\rho $$ ρ -estimation

2016

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Abstract. The aim of this paper is to present a new estimation procedure that can be applied in various statistical frameworks including density and regression and which leads to both robust and optimal (or nearly optimal) estimators. In density estimation, they asymptotically coincide with the celebrated maximum likelihood estimators at least when the statistical model is regular enough and contains the true density to estimate. For very general models of densities, including non-compact ones, these estimators are robust with respect to the Hellinger distance and converge at optimal rate (up to a possible logarithmic factor) in all cases we know. In the regression setting, our approach improves upon the classical least squares in many respects. In simple linear regression for example, it provides an estimation of the coefficients that are both robust to outliers and simultaneously rateoptimal (or nearly rate-optimal) for a large class of error distributions including Gaussian, Laplace, Cauchy and uniform among others.

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Section: Connection With the Maximum Likelihood Estimatormentioning

confidence: 83%

A new method for estimation and model selection: $$\rho $$ ρ -estimation

2016

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“…recently, Sart has applied robust tests in the special cases of dyadic partition selection(Sart, 2012) and parameter selection(Sart, 2013) …”

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confidence: 99%

An efficient algorithm for T-estimation

Magalhães¹,

Rozenholc²

2014

Preprint

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We introduce an efficient and exact algorithm, together with a faster but approximate version, which implements with a sub-quadratic complexity the hold-out derived from T-estimation. We study empirically the performance of this hold-out in the context of density estimation considering well-known competitors (hold-out derived from least-squares or Kullback-Leibler divergence, model selection procedures, etc.) and classical problems including histogram or bandwidth selection. Our algorithms are integrated in a companion R-package called Density.T.HoldOut available on the CRAN: http://cran.r-project.org/web/packages/Density. T.HoldOut/index.html.

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Governing the Contested City: Geographies of Displacement in Diyarbakır, Turkey

Genç

2021

Antipode

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This article develops a spatial analysis of the authoritarian governmentality of the Kurdish issue in Turkey through a case study of Diyarbakır in which geographies of war and peace have coalesced. Drawing on recent theorisations of military urbanism, it examines encounters-including both conflict and cooperation-between the state bureaucracy, local pro-government elite and pro-Kurdish municipal authorities within the context of the initiatives to rehabilitate the city's historic centre, Suric ßi, during the period 2002-2015. The article suggests considering Suric ßi's rehabilitation as part of a post-conflict urban regime to thwart social and political vibrancy of the Kurdish urban poor in tandem with the idea of securitisation through marketisation. In doing so, it demonstrates that unpacking spatial imaginaries and rationalities that have rendered Suric ßi dwellers displaceable by devaluing their collective urban experience is critical for our understanding of the utter violence erupted during and after the urban warfare of 2015.

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Robust estimation on a parametric model via testing

Cited by 3 publications

References 27 publications

A new method for estimation and model selection: $$\rho $$ ρ -estimation

A new method for estimation and model selection: $$\rho $$ ρ -estimation

An efficient algorithm for T-estimation

Governing the Contested City: Geographies of Displacement in Diyarbakır, Turkey

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