Calibration with Many Variables
研究了当响应变量维度很高（约1000）而观测数很少时，如何通过降维方法获得唯一的预测值，适用于食品和化工行业。
Multivariate calibration involves the use of an estimated relationship between a multivariate response vector Y and an explanatory vector X to predict unknown X in future from further observed responses. With modern instrumentation the dimension of the response vector may be very large (of the order 1000) and yet the number of observations small. Under such circumstances standard approaches to calibration give rise to non-unique predictors of future X. To obtain a unique estimator it is necessary to impose additional structure. We investigate various approaches to dimension reduction to do this. Areas of application are the food and chemical industries