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利用尖峰总体特征值估计脑磁图信号源数量AI 解读

Magnetoencephalography (MEG) is an advanced imaging technique used to measure the magnetic fields outside the human head produced by the electrical activity inside the brain. Various source localization methods in MEG require the knowledge of the underlying active sources, which are identified by a priori. Common methods used to estimate the number of sources include principal component analysis or information criterion methods, both of which make use of the eigenvalue…

油气钻井成本：约束稳健回归的应用AI 解读

The robust regression method of Huber (1973) is used to fit a model to the cost of drilling for petroleum. Because the model includes a categorical variable (well type), a linear constraint is imposed on the parameter estimates Because the model was fit to the logarithm of cost and because it will be used to make repeated predictions of cost, an adjustment that approximately unbiases the predictions is imposed. The numerical values of the estimates are discussed, and a…

石油与天然气钻井成本：约束稳健回归的应用AI 解读

The robust regression method of Huber (1973) is used to fit a model to the cost of drilling for petroleum. Because the model includes a categorical variable (well type), a linear constraint is imposed on the parameter estimates Because the model was fit to the logarithm of cost and because it will be used to make repeated predictions of cost, an adjustment that approximately unbiases the predictions is imposed. The numerical values of the estimates are discussed, and a…

众数的最优核估计AI 解读

Let $X_1, \cdots, X_n$ be independent observations with common density $f$. A kernel estimate of the mode is any value of $t$ which maximizes the kernel estimate of the density $f_n$. Conditions are given restricting the density, the kernel, and the bandwidth under which this estimate of the mode has an asymptotic normal distribution. By imposing sufficient restrictions, the rate at which the mean squared error of the estimator converges to zero can be decreased from $n^{-\frac{4}{7}}$…

面向预测的模型选择方法AI 解读

This article offers a synthesis of Bayesian and sample-reuse approaches to the problem of high structure model selection geared to prediction. Similar methods are used for low structure models. Nested and nonnested paradigms are discussed and examples given.