Loss Functions and Risk
Screening Data Points in Empirical Risk Minimization via Ellipsoidal Regions and Safe Loss Functions
... Abstract We design simple screening tests to automatically discard data samples in empirical risk minimization without losing optimization guarantees. We derive loss functions that produce dual ...
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Differentially Private Empirical Risk Minimization with Smooth Non-Convex Loss Functions: A Non-Stationary View
... convex loss functions, starting from the work in (Chaudhuri and Monteleoni ...non-convex loss functions can achieve better classification accuracy than convex loss functions ...
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Forecasting Nonlinear Functions of Returns Using LINEX Loss Functions
... symmetric loss functions to evaluate the efficacy of ...asymmetric loss functions have concentrated on the return pro- ...symmetric loss function is not, in general, appropriate, and ...
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Loss functions, utility functions and Bayesian sample size determination.
... blinex loss given in (1.13) the posterior risk function is given in the equation ...blinex loss function for different bounding parameter γ and shape parameter ...
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Forecasting volatility using LINEX loss functions
... Market Risk Premia Using Higher Moments, WP99-17 ...Market Risk and the Concept of Fundamental Volatility: Measuring Volatility Across Asset and Derivative Markets and Testing for the Impact of Derivatives ...
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Proportional loss functions for debris flow events
... Quantitative risk assessments of debris flows and other hydrogeological hazards require the analyst to predict damage ...proportional loss func- tions that may be used for various prediction purposes ...
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Iterative Regularization for Learning with Convex Loss Functions
... The above results give finite sample bounds on the excess risk, provided that a suitable stopping rule is considered. While the stopping rule in the above theorems is distribution dependent, a data-driven stopping ...
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Transformation kernel density estimation of actuarial loss functions
... Abstract: A transformation kernel density estimator that is suitable for heavy-tailed distributions is discussed. Using a truncated Beta transformation, the choice of the bandwidth parameter becomes straightforward. An ...
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Loss Functions in Restricted Parameter Spaces and Their Bayesian Applications
... Brown’s loss function [4] itself, D + (θ, d). One can check that all of functions above are scale invariant, but only Brown’s loss function is scale symmetric and infinitely penalizes the boundary ...
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Adaptability and transferability of flood loss functions in residential areas
... Flood loss modeling is an important component within flood risk ...stage-damage functions are used for the estimation of direct monetary dam- age to ...such functions are governed by large ...
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Improved estimation for linear models under different loss functions
... For the analyses of the risk functions of the estimators, analytical, graphical and numerical procedures are adopted. In Part I the SRE, SPTE and SE of the slope and intercept parameters of the simple ...
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Improved estimation for linear models under different loss functions
... Chapter 1. Introduction 2 knowledge. The prior distribution of a parameter is used in the Bayesian approach to statistical analysis. However, if the prior information about the parameter is available as a constant value ...
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Conditional Value-at-Risk Constraint and Loss Aversion Utility Functions
... incorporating risk measures as constraints in a classic expected return maximization ...of risk measures, we show that if the decision maker (DM) max- imizes the expectation of a random return under ...
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Loss-Based Risk Measures
... quantile functions that are continuous on (0, 1) in order to avoid irregularities due to the presence of ...quantile functions. Indeed, people do assume the continuity of quantile functions in many ...
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Risk-based profit and loss attribution
... specific risk profile. This does not vary smoothly with the risk factors when the ‘biting’ calculation ...step functions of the risk factors and second-order sensitivities that are Dirac delta ...
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Are analysts' loss functions asymmetric?
... significance of ERRVAR, with ERRSKEW having the same sign as ERRVAR. This is exactly what we find in our results. 4.4 Book-to-market and size portfolio analysis The results reported in table 2 are based on a very large ...
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Are analysts’ loss functions asymmetric?
... significance of ERRVAR, with ERRSKEW having the same sign as ERRVAR. This is exactly what we find in our results. 4.4 Book-to-market and size portfolio analysis The results reported in table 2 are based on a very large ...
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Estimating loss functions of experts
... symmetric loss functions ...two loss functions, we find overwhelming support for the conjecture that experts may feel that negative forecast errors (meaning the forecasts are below actual ...
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Loss reserving with kernel functions
... Abstract. Loss reserving is a fundamental concept of actuarial mathematics. A traditionally used method is the chain ladder method. While it is a simple and robust method and works well in many cases, it also has ...
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Confidence Intervals and Loss Functions
... the loss function analysis tilted the comparisons in one direction or ...the loss function results, though they had some ...the loss functions regarding synthetic error would be expected to be ...
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