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Related Experiment Video

Updated: Sep 5, 2025

Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction
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On The Randomized Schmitter Problem.

Hansjörg Albrecher1, José Carlos Araujo-Acuna2

  • 1Department of Actuarial Science, Faculty of Business and Economics, University of Lausanne, Switzerland and Swiss Finance Institute, Lausanne, Switzerland.

Methodology and Computing in Applied Probability
|July 5, 2022
PubMed
Summary
This summary is machine-generated.

This study simplifies ruin theory problems by randomizing the initial surplus. This approach provides analytical bounds for ruin probability and approximates deterministic solutions using exponential and Erlang distributions.

Keywords:
ErlangizationLaplace transformRuin probabilityRuin theorySchmitter problemStochastic ordering

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Area of Science:

  • Actuarial Science
  • Probability Theory
  • Risk Management

Background:

  • The classical Schmitter problem in ruin theory traditionally assumes a deterministic initial surplus.
  • Computational complexity arises in analyzing ruin probabilities with varying initial surplus levels.

Purpose of the Study:

  • To revisit the Schmitter problem by introducing a randomly chosen initial surplus.
  • To develop simplified analytical bounds for ruin probabilities using randomization.
  • To approximate solutions for deterministic initial surplus using this new approach.

Main Methods:

  • Randomization of the initial surplus level (U).
  • Utilizing exponentially distributed U for computational simplification.
  • Connecting the problem to m-convex ordering.
  • Approximating deterministic solutions with Erlang(k)-distributed U for large k.

Main Results:

  • Derived simple and sharp analytical bounds for ruin probability.
  • Established a connection between the randomized Schmitter problem and m-convex ordering.
  • Demonstrated the utility of randomization for computational simplification.
  • Showed that Erlang(k)-distributed U approximates deterministic solutions effectively.

Conclusions:

  • Randomizing the initial surplus offers significant computational advantages in ruin theory.
  • The m-convex ordering provides a powerful tool for deriving bounds on ruin probabilities.
  • The proposed method offers an efficient way to approximate classical ruin problems.