Suppose the p-variate random vector W, partitioned into q variables W-1 and p - q variables W-2, follows a multivariate normal mixture distribution. If the investigator is mainly interested in estimation of the
The intrinsic dimensionality is, in essence, a local characteristic of the distribution, as shown in Fig. 6-4. If we establish small local regions around X1, X2, X3, etc., the dimensionality within the local region must be close to 1 [19],[20]. Because of this, the intrinsic dimensio...
Martingale limit theorems are also used to demonstrate that the expected probability of correct classification tends monotonically to unity for two general classification problems.doi:10.1080/03610928508805140KokolakisGeorge E.Marcel Dekker, Inc.Communications in Statistics...
Thermal resolution (also referred to as temperature uncertainty) establishes the minimum discernible temperature change sensed by luminescent thermometers and is a key figure of merit to rank them. Much has been done to minimize its value via probe optim
Amazon.com: Approximate Dynamic Programming: Solving the Curses of Dimensionality (Wiley Series in Probability and Statistics): 9780470171554: Warren B. Powell: Books
When one is interested only in prediction, the COD is less of a problem for future data whose explanatory variables have values close to those observed in the past. But an unobvious consequence of large p is that nearly all new observation vectors tend to be far from those previously seen....
of samples or observations [1]. These datasets can be computationally expensive to learn and generating mapping functions between input and output can be a cumbersome task. Thus, reducing the number of features, or the problem dimensionality, can greatly simplify the learning and training of ...
Electric load forecasting is crucial in the planning and operating electric power companies. It has evolved from statistical methods to artificial intelligence-based techniques that use machine learning models. In this study, we investigate short-term lo
Particularly in the field of industrial processes, the robustness to out-of-sample data of dimensionality reduction techniques can also be a crucial metric. For online process monitoring, dimensionality reduction models should be trained offline using training data, with the requirement to ensure robustn...
The proposed procedure, a large sample method, gives strongly consistent estimates of the population model for a wide class of criterion functions. In this paper, we report the simulation results of variable selection problem. The criterion functions is a class of functions of the sample size and...