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人工智能驱动的特征价格与质量调整价格指数AI 解读

We develop empirical models that efficiently process large amounts of unstructured product data (text, images, prices, quantities) to produce accurate hedonic price estimates and derived indices. To achieve this, we generate abstract product attributes (or “features”) from descriptions and images using deep neural networks. These attributes are then used to estimate the hedonic price function. To demonstrate the effectiveness of this approach, we apply the models to Amazon’s…

通过交叉验证一致选择聚类数AI 解读

In cluster analysis, one of the major challenges is to estimate the number of clusters. Most existing approaches attempt to minimize some distance-based dissimilarity measure within clusters. This article proposes a novel selection criterion that is applicable to all kinds of clustering algorithms, including distance based or non-distance based algorithms. The key idea is to select the number of clusters that minimizes the algorithm's instability, which measures the…

大间隔分类器的类别概率估计AI 解读

Large margin classifiers have proven to be effective in delivering high predictive accuracy, particularly those focusing on the decision boundaries and bypassing the requirement of estimating the class probability given input for discrimination. As a result, these classifiers may not directly yield an estimated class probability, which is of interest itself. To overcome this difficulty, this article proposes a novel method for estimating the class probability through…

为回归曲线检验获得最优功效的平滑参数选择AI 解读

We consider selection of smoothing parameters to obtain optimal power in tests of regression curves. We examine three tests and propose empirical smoothing parameters to maximize the power in each test. We also show that the data-based smoothing parameters converge to the optimal smoothing parameters as sample sizes get larger. We conduct a simulation study for various classes of alternative showing the effectiveness of the proposed procedures.