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In this paper, an alternative Discrete skew Logistic distribution is proposed, which is derived by using the general approach of discretizing a continuous distribution while retaining its survival function. The properties of the…

Methodology · Statistics 2016-04-07 Deepesh Bhati , Subrata Chakraborty , Snober Gowhar Lateef

We study the utility indifference price of a European option in the context of small transaction costs. Considering the general setup allowing consumption and a general utility function at final time T, we obtain an asymptotic expansion of…

Optimization and Control · Mathematics 2015-04-07 Dylan Possamaï , Guillaume Royer

We consider the problem of estimating the missing mass, partition function or evidence and its probability distribution in the case that for each sample point in the discrete sample space its (unnormalized) probability mass is revealed.…

Statistics Theory · Mathematics 2026-03-16 Bastiaan J. Braams

This paper develops a new method for identifying econometric models with partially latent covariates. Such data structures arise in industrial organization and labor economics settings where data are collected using an input-based sampling…

Econometrics · Economics 2022-06-29 Minji Bang , Wayne Yuan Gao , Andrew Postlewaite , Holger Sieg

We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine example-critiquing, a technique for enabling users to incrementally construct preference models…

Artificial Intelligence · Computer Science 2011-10-04 B. Faltings , P. Pu , P. Viappiani

Recent advances in language modeling have underscored the role of preference feedback in enhancing model performance. This paper investigates the conditions under which preference feedback improves parameter estimation in classes of…

Machine Learning · Statistics 2025-05-30 Marc Jourdan , Gizem Yüce , Nicolas Flammarion

We present a new algorithm to optimize distributions defined implicitly by parameterized stochastic diffusions. Doing so allows us to modify the outcome distribution of sampling processes by optimizing over their parameters. We introduce a…

Using deep neural nets as function approximator for reinforcement learning tasks have recently been shown to be very powerful for solving problems approaching real-world complexity. Using these results as a benchmark, we discuss the role…

Machine Learning · Computer Science 2016-01-21 Vincent François-Lavet , Raphael Fonteneau , Damien Ernst

We show that discounted methods for solving continuing reinforcement learning problems can perform significantly better if they center their rewards by subtracting out the rewards' empirical average. The improvement is substantial at…

Machine Learning · Computer Science 2024-10-31 Abhishek Naik , Yi Wan , Manan Tomar , Richard S. Sutton

Employee theft and dishonesty is a major contributor to loss in the retail industry. Retailers have reported the need for more automated analytic tools to assess the liability of their employees. In this work, we train and optimize several…

Machine Learning · Computer Science 2018-03-30 Richard R. Yang , Mike Borowczak

Discount is the difference between the face value of a bond and its present value. I propose an arbitrage-free dynamic framework for discount models, which provides an alternative to the Heath--Jarrow--Morton framework for forward rates. I…

Mathematical Finance · Quantitative Finance 2023-07-28 Damir Filipovic

We develop a novel Empirical Bayes methodology for prediction under check loss in high-dimensional Gaussian models. The check loss is a piecewise linear loss function having differential weights for measuring the amount of underestimation…

Statistics Theory · Mathematics 2016-06-24 Gourab Mukherjee , Lawrence D. Brown , Paat Rusmevichientong

Poor sample efficiency is a major limitation of deep reinforcement learning in many domains. This work presents an attention-based method to project neural network inputs into an efficient representation space that is invariant under…

Machine Learning · Computer Science 2020-03-23 John Mern , Dorsa Sadigh , Mykel J. Kochenderfer

We consider indifference pricing of contingent claims consisting of payment flows in a discrete time model with proportional transaction costs and under exponential disutility. This setting covers utility maximisation as a special case. A…

Mathematical Finance · Quantitative Finance 2021-05-25 Alet Roux , Zhikang Xu

We propose a criterion of equidistribution by the differentiability of certain arithmetic invariants. Combined with the slope method and the asymptotic measures, this criterion gives a new "conceptual" proof to equidistribution results…

Algebraic Geometry · Mathematics 2008-12-19 Huayi Chen

In a Markovian stochastic volatility model, we consider financial agents whose investment criteria are modelled by forward exponential performance processes. The problem of contingent claim indifference valuation is first addressed and a…

Portfolio Management · Quantitative Finance 2016-11-26 Michail Anthropelos

Probit models are useful for modeling correlated discrete responses in many disciplines, including consumer choice data in economics and marketing. However, the Gaussian latent variable feature of probit models coupled with identification…

Methodology · Statistics 2024-09-30 Patrick Ding , Guido Imbens , Zhaonan Qu , Yinyu Ye

Recovering and distinguishing between the strict-preference, indifference and/or indecisiveness parts of a decision maker's preferences is a challenging task but also important for testing theory and conducting welfare analysis. This paper…

Theoretical Economics · Economics 2025-09-15 Georgios Gerasimou

In this article, we develop a semiparametric Bayesian estimation and model selection approach for partially linear additive models in conditional quantile regression. The asymmetric Laplace distribution provides a mechanism for Bayesian…

Computation · Statistics 2013-07-11 Yuao Hu , Kaifeng Zhao , Heng Lian

A survival model is derived from the exponential function using the concept of fractional differentiation. The hazard function of the proposed model generates various shapes of curves including increasing, increasing-constant-increasing,…

Statistics Theory · Mathematics 2007-06-13 Cheng K. Lee , Jenq-Daw Lee
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