Publications related to Smoothing Spline Distribution Function Estimation: Theory

From Data to Decisions: Distributionally Robust Optimization is Optimal

We study stochastic programs where the decision-maker cannot observe the distribution of the exogenous uncertainties but has access to a finite set of independent samples from this distribution. In this setting, the goal is to find a procedure that transfo ...

2020

Max-infinitely divisible models and inference for spatial extremes

Emeric Rolland Georges Thibaud, Raphaël Huser

For many environmental processes, recent studies have shown that the dependence strength is decreasing when quantile levels increase. This implies that the popular max-stable models are inadequate to capture the rate of joint tail decay, and to estimate jo ...

WILEY2020

Statistical Aspects of Wasserstein Distances

Victor Panaretos, Yoav Zemel

Wasserstein distances are metrics on probability distributions inspired by the problem of optimal mass transportation. Roughly speaking, they measure the minimal effort required to reconfigure the probability mass of one distribution in order to recover th ...

ANNUAL REVIEWS2019

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

Daniel Kuhn, Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh, Wolfram Wiesemann

The likelihood function is a fundamental component in Bayesian statistics. However, evaluating the likelihood of an observation is computationally intractable in many applications. In this paper, we propose a non-parametric approximation of the likelihood ...

2019

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Fei Pu

In this thesis, we study systems of linear and/or non-linear stochastic heat equations and fractional heat equations in spatial dimension

1

driven by space-time white noise. The main topic is the study of hitting probabilities for the solutions to these ...

EPFL2018

Multilevel Monte Carlo Approximation of Functions

Fabio Nobile, Sebastian Krumscheid

Many applications across sciences and technologies require a careful quantification of nondeterministic effects to a system output, for example, when evaluating the system’s reliability or when gearing it towards more robust operation conditions. At the he ...

2018

MATHICSE Technical Report : Multilevel Monte Carlo approximation of functions

Fabio Nobile, Sebastian Krumscheid

Many applications across sciences and technologies require a careful quantification of non-deterministic effects to a system output, for example when evaluating the system's reliability or when gearing it towards more robust operation conditions. At the he ...

MATHICSE2017

A general framework for routing problems with stochastic demands

Michel Bierlaire, Sacha Varone, Mohammad Yousef Maknoon, Iliya Dimitrov Markov

We introduce a unified modeling and solution framework for various classes of rich vehicle and inventory routing problems as well as other probability-based routing problems with a time-horizon dimension. Demand is assumed to be stochastic and non-stationa ...

2017

A unified framework for rich routing problems with stochastic demands

Michel Bierlaire, Sacha Varone, Mohammad Yousef Maknoon, Iliya Dimitrov Markov

We introduce a unified framework for rich vehicle and inventory routing problems with complex physical and temporal constraints. Demands are stochastic, can be non-stationary, and are forecast using any model that provides the expected demands and their er ...

2017

Updated braking forces for the assessment of road bridges

João Miguel De Oliveira Durães Alves Martins

Safety assessments of road bridges to braking events combine the braking force, acting along the longitudinal axis of the deck, with a vertical load that accounts for the vertical component of the traffic action. In modern design standards the vertical loa ...

EPFL2016

Smoothing Spline Distribution Function Estimation: Theory

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From Data to Decisions: Distributionally Robust Optimization is Optimal

Max-infinitely divisible models and inference for spatial extremes

Statistical Aspects of Wasserstein Distances

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Multilevel Monte Carlo Approximation of Functions

MATHICSE Technical Report : Multilevel Monte Carlo approximation of functions

A general framework for routing problems with stochastic demands

A unified framework for rich routing problems with stochastic demands

Updated braking forces for the assessment of road bridges

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation

Statistical Aspects of Wasserstein Distances

Max-infinitely divisible models and inference for spatial extremes

From Data to Decisions: Distributionally Robust Optimization is Optimal

MATHICSE Technical Report : Multilevel Monte Carlo approximation of functions

Multilevel Monte Carlo Approximation of Functions

Updated braking forces for the assessment of road bridges

A unified framework for rich routing problems with stochastic demands

A general framework for routing problems with stochastic demands