损失函数(loss function)或代价函数(cost function)是将随机事件或其有关随机变量的取值映射为非负实数以表示该随机事件的“风险”或“损失”的函数。在应用中,损失函数通常作为学习准则与优化问题相联系,即通过最小化损失函数求解和评估模型。例如在统计学和机器学习中被用于模型的参数估计(parameteric estimation) [1...
parameter estimationprediction errorestimation theorysignal processingThe problem addressed in this note concerns the relationship between the minimizers of a given loss function parametrized in two different ways. The so-called "invariance principle" (IP) gives a simple answer to this problem in a ...
损失函数（loss function）或代价函数（cost function）是将随机事件或其有关随机变量的取值映射为非负实...
notation, but most of the functions we present can be used both in estimation and in prediction. It is important to note thatwe can always multiply a loss function by a positive constant and/or add an arbitrary constant to it. These transformations do not change model rankings and the resul...
I(y_i – ŷ_i < 0) is an indicator function that equals 1 if y_i – ŷ_i is less than 0 (indicating an underestimation) and 0 otherwise. Benefits Flexibility in Quantile Estimation Tail Behavior Analysis Statistical Inference Drawbacks Non-Convex Optimization Larger Data Requirement Incre...
The problem of Bayes sequential estimation of the mean in one-parameter exponential family with LINEX (linear-exponential) loss function and fixed cost for each observation is considered in this article. Given a prior, a family of stopping times is proposed and shown to be asymptotically ...
Specify the Regularization option in the estimation option sets. For linear-in-parameter models (FIR models) and ARX models, you can compute optimal values of the regularization variables R and λ using the arxRegul command. Effect of Focus and WeightingFilter Options on the Loss Function The Fo...
"Reliability Estimation in Inverse Ray- leigh Distribution using Precautionary Loss Function", Mathematics and Statis- tics Journal, ISSN-2077-4591, 2(3): 9-15.Rasheed, H.A. and R. Kh Aref, 2016. "Reliability Estimation in Inverse Rayleigh Distribution using Precautionary Loss Function", ...
If we simplify the problem and replace the neural network by a simple linear regression with three parameters, one for each dimension of the state space and a bias parameter, we can visualize the loss function across any axis. If bias and weight for state variable x from the example in ...
Preliminary-test estimation of the regression scale parameter when the loss function is asymmetric. Communications in Statistics-Theory and Methods 22(6):1709-1733.Giles, Judith A.; Giles, David E.A. (1993). Preliminary-test estimation of the regression scale parameter when the loss function is...