We present a new functional Bayes classifier that uses principal component (PC) or partial least squares (PLS) scores from the common covariance function, that is, the covariance function marginalized over groups. When the groups have different covariance functions, the PC or PLS scores need not be independent or even uncorrelated. We use copulas to model the dependence. Our method is semiparametric; the marginal densities are estimated nonparametrically by kernel smoothing and the copula is modeled parametrically. We focus on Gaussian and t-copulas, but other copulas could be used. The strong performance of our methodology is demonstrated through simulation, real data examples, and asymptotic properties.

翻译：我们提出了一个新的功能性贝叶分类器,使用共同共变函数的主要成分(PC)或部分最小平方分数(PLS),即共变函数,在群体上处于边缘地位。当群体具有不同的共变函数时,PC或PLS分数不一定独立,甚至不相关。我们用阳极来模拟依赖性。我们的方法是半等分法;边际密度是非对称性的,通过内空滑动估算,对焦云进行模拟。我们侧重于高斯和高普尔,但可以使用其他合差。我们方法的强效表现通过模拟、真实数据实例和无药性来证明。