Pages that link to "Item:Q1018640"
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The following pages link to Consistency of restricted maximum likelihood estimators of principal components (Q1018640):
Displaying 20 items.
- A randomness test for functional panels (Q311801) (← links)
- Robust functional sliced inverse regression (Q513702) (← links)
- Functional \(k\)-means inverse regression (Q1615228) (← links)
- Nonparametric operator-regularized covariance function estimation for functional data (Q1615269) (← links)
- Optimal weighting schemes for longitudinal and functional data (Q1642270) (← links)
- Data depth for measurable noisy random functions (Q1733273) (← links)
- Principal components analysis for sparsely observed correlated functional data using a kernel smoothing approach (Q1952256) (← links)
- A partition Dirichlet process model for functional data analysis (Q2040661) (← links)
- Two-sample inference for sparse functional data (Q2044341) (← links)
- Analysis of multivariate non-Gaussian functional data: a semiparametric latent process approach (Q2078559) (← links)
- Spline estimation of functional principal components via manifold conjugate gradient algorithm (Q2103990) (← links)
- Surface functional models (Q2201551) (← links)
- Asymptotic properties of penalized splines for functional data (Q2203625) (← links)
- Covariate adjusted functional principal components analysis for longitudinal data (Q2380101) (← links)
- Consistency of the mean and the principal components of spatially distributed functional data (Q2435212) (← links)
- Asymptotic Conditional Singular Value Decomposition for High-Dimensional Genomic Data (Q3013964) (← links)
- A Smooth Simultaneous Confidence Corridor for the Mean of Sparse Functional Data (Q4975407) (← links)
- Functional sufficient dimension reduction based on weighted method (Q5055178) (← links)
- Low-Rank Covariance Function Estimation for Multidimensional Functional Data (Q5885105) (← links)
- Penalized spline estimation of principal components for sparse functional data: rates of convergence (Q6589574) (← links)
