Pages that link to "Item:Q4380845"
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The following pages link to An approximated principal component prediction model for continuous time stochastic processes (Q4380845):
Displaying 17 items.
- Predicting the continuation of a function with applications to call center data (Q389291) (← links)
- Regression models for functional data by reproducing kernel Hilbert spaces methods (Q866626) (← links)
- PLS regression on a stochastic process (Q957099) (← links)
- Clusterwise PLS regression on a stochastic process (Q957186) (← links)
- Variational Bayesian functional PCA (Q961145) (← links)
- Statistical inference for doubly stochastic multichannel Poisson processes: a PCA approach (Q961932) (← links)
- Forecasting binary longitudinal data by a functional PC-ARIMA model (Q1023652) (← links)
- Forecasting with unequally spaced data by a functional principal component approach (Q1302071) (← links)
- Wavelet methods for continuous-time prediction using Hilbert-valued autoregressive processes (Q1414608) (← links)
- Forecasting counting and time statistics of compound Cox processes: a focus on intensity phase type process, deletions and simultaneous events (Q2066496) (← links)
- Selection of time instants and intervals with support vector regression for multivariate functional data (Q2664383) (← links)
- Stochastic modelling for evolution of stock prices by means of functional principal component analysis (Q2711689) (← links)
- Forecasting PC-ARIMA Models for Functional Data (Q3298643) (← links)
- Forecasting Pollen Concentration by a Two-Step Functional Model (Q3576936) (← links)
- Principal component estimation of functional logistic regression: discussion of two different approaches (Q4831081) (← links)
- Computational approaches to estimation in the principal component analysis of a stochastic process (Q4940117) (← links)
- Functional Principal Component Regression and Functional Partial Least‐squares Regression: An Overview and a Comparative Study (Q6064648) (← links)
