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Andreas Andresen

Publications and source records attributed to Andreas Andresen.

7 recordsLinked to original sources
arXiv2025

Real-time Optimization of Transport Chains for Single Wagon Load Railway Transport

The freight branch of the Swiss national railways, SBB Cargo, offers customers to ship single or few wagons within its wagon load transportation system (WLV). In this system, wagons travel along a transport chain which is a sequence of consecutive trains. Recently, SBB Cargo redesigned its IT systems and renewed the computation of these transport chains. This paper describes the main design decisions and technical details: data structures, search algorithms, mathematical optimization of throughput in the real-time setting, and some selected details for making the algorithms work in the operational software. We also comment on the employed technology stack and finally demonstrate some performance metrics from running operations.

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arXiv2015

Finite sample analysis of profile M-estimation in the Single Index model

We apply the results of Andresen A. and Spokoiny V. on profile M-estimators and the alternating maximization procedure to analyse a sieve profile quasi maximum likelihood estimator in the single index model with linear index function. The link function is approximated with \(C^3\)-Daubechies-wavelets with compact support. We derive results like Wilks phenomenon and Fisher Theorem in a finite sample setup. Further we show that an alternation maximization procedure converges to the global maximizer and assess the performance of a projection pursuit procedure in that context. The approach is based on showing that the conditions of Andresen A. and Spokoiny V. on profile M-estimators and the alternating maximization procedure can be satisfied under a set of mild regularity and moment conditions on the index function, the regressors and the additive noise. This allows to construct nonasymptotic confidence sets and to derive asymptotic bounds for the estimator as corollaries.

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arXiv2015

Two convergence results for an alternation maximization procedure

Andresen and Spokoiny's (2013) ``critical dimension in semiparametric estimation`` provide a technique for the finite sample analysis of profile M-estimators. This paper uses very similar ideas to derive two convergence results for the alternating procedure to approximate the maximizer of random functionals such as the realized log likelihood in MLE estimation. We manage to show that the sequence attains the same deviation properties as shown for the profile M-estimator in Andresen and Spokoiny (2013), i.e. a finite sample Wilks and Fisher theorem. Further under slightly stronger smoothness constraints on the random functional we can show nearly linear convergence to the global maximizer if the starting point for the procedure is well chosen.

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arXiv2014

A note on critical dimensions in profile semiparametric estimation

This paper complements the results of Andresen et. al "Critical dimension in profile semiparametric estimation" (2014) on profile estimators in semiparametric models. We present two examples. One that illustrates that the smoothness constraint on the expected value of the contrast functional used to define the profile M-estimator is necessary for the bound derived for the critical ratio of dimension to sample size. A second one to show that in the case that the target dimension is proportional to the full dimension the critical ratio for the Fisher type result stays the same while for the Wilks phenomenon it is multiplied with the square root of the full dimension, just as in the upper bound in Andresen et. al (2014).

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arXiv2014

Critical dimension in profile semiparametric estimation

This paper revisits the classical inference results for profile quasi maximum likelihood estimators (profile MLE) in the semiparametric estimation problem. We mainly focus on two prominent theorems: the Wilks phenomenon and Fisher expansion for the profile MLE are stated in a new fashion allowing finite samples and model misspecification. The method of study is also essentially different from the usual analysis of the semiparametric problem based on the notion of the hardest parametric submodel. Instead we derive finite sample deviation bounds for the linear approximation error for the gradient of the loglikelihood. This novel approach particularly allows to address the important issue of the effective target and nuisance dimension. The obtained nonasymptotic results are surprisingly sharp and yield the classical asymptotic statements including the asymptotic normality and efficiency of the profile MLE. The general results are specified to the important special cases of an i.i.d. sample and the analysis is exemplified with a single index model.

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