Nonparametric Adverse Selection Problems

نویسنده

  • Guillaume Carlier
چکیده

This article is devoted to adverse selection problems in which individual private information is a whole utility function and cannot be reduced to some nite-dimensional parameter. In this case, incentive compatibility conditions can be conveniently expressed using some abstract convexity notions arising in Mass Transfer Theory 8]. After this characterization is provided, an existence result of optimal incentive-compatible contracts is proved. Finally, several economic examples are considered including applications to regulation and labor contracting.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Nonparametric statistical inverse problems

We explain some basic theoretical issues regarding nonparametric statistics applied to inverse problems. Simple examples are used to present classical concepts such as the white noise model, risk estimation, minimax risk, model selection and optimal rates of convergence, as well as more recent concepts such as adaptive estimation, oracle inequalities, modern model selection methods, Stein’s unb...

متن کامل

The Effect of Deviation from Optimal Cash Level on Adverse Selection and Moral Hazard in Firms Listed on Tehran Stock Exchange

This study aims to investigate the impact of deviation from optimal level of cash holdings on adverse selection and moral hazard problems. The data set includes 106 listed firms of Tehran Stock Exchange during the period of 2005-2016 and both panel data and cross-sectional data multivariate regressions were utilized in different stage of analysis to test the hypotheses. According to the optimal...

متن کامل

Bayesian Nonparametric Models

A Bayesian nonparametric model is a Bayesian model on an infinite-dimensional parameter space. The parameter space is typically chosen as the set of all possible solutions for a given learning problem. For example, in a regression problem the parameter space can be the set of continuous functions, and in a density estimation problem the space can consist of all densities. A Bayesian nonparametr...

متن کامل

Is Nonparametric Learning Practical in Very High Dimensional Spaces?

Many of the challenges faced by the field of Computational Intelligence in building intell igent agents, involve determining mappings between numerous and varied sensor inputs and complex and flexible action sequences. In applying nonparametric learning techniques to such problems we must therefore ask: "Is nonparametric learning practical in very high dimensional spaces?" Contemporary wisdom s...

متن کامل

Automatic Smoothing and Variable Selection via Regularization

This thesis focuses on developing computational methods and the general theory of automatic smoothing and variable selection via regularization. Methods of regularization are a commonly used technique to get stable solution to ill-posed problems such as nonparametric regression and classification. In recent years, methods of regularization have also been successfully introduced to address a cla...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Annals OR

دوره 114  شماره 

صفحات  -

تاریخ انتشار 2002