搜索结果: 1-15 共查到“Model selection”相关记录66条 . 查询时间(0.203 秒)
Impact of plot size and model selection on forest biomass estimation using airborne LiDAR: A case study of pine plantations in southern Spain
airborne laser scanning forest inventory regression survey design genetic selection methods
2017/4/6
We explored the usefulness of LiDAR for modelling and mapping the stand biomass of two conifer species in southern Spain. We used three different plot sizes and two statistical approaches (i.e. stepwi...
Inversion of synthetic geodetic data for the 1997 Colfiorito events:clues on the effects of layering,assessment of model parameter PDFs,and model selection criteria
data inversion geodesy coseismic displacement
2015/9/1
The 1997 September-October Umbria-Marche sequence has been extensively studied in the past by analyzing coseismic displacement data (GPS, leveling, SAR). Here we focus on synthetic data representative...
Estimation and Accuracy after Model Selection
model averaging Cp, Lasso bagging bootstrap smoothing
2015/8/20
Classical statistical theory ignores model selection in assessing estimation accuracy. Here we consider
bootstrap methods for computing standard errors and con dence intervals that take model selecti...
Classical statistical theory ignores model selection in assessing estimation accuracy. Here we consider
bootstrap methods for computing standard errors and con dence intervals that take model selecti...
Near-ideal model selection by `1 minimization
Model selection oracle inequalities the lasso compressed sensing incoherence eigenvalues of random matrices
2015/6/17
We consider the fundamental problem of estimating the mean of a vector y = Xβ + z, where X is an n× p design matrix in which one can have far more variables than observations and z is a stochastic err...
Discussion of “Latent Variable Graphical Model Selection via Convex Optimization”
Latent Variable Graphical Model Selection Convex Optimization
2015/6/17
We wish to congratulate the authors for their innovative contribution, which is bound to inspire much further research. We find latent variable model selection to be a fantastic application of matrix ...
Stereo Model Selection and Point Cloud Filtering using an Out-of-Core Octree
Matching Point Cloud Photogrammetry Processing Integration Fusion Sampling
2014/9/9
Dense image matching methods enable the retrieval of dense surface information using any kind of imagery. The best quality can be achieved for highly overlapping datasets, which avoids occlusions and ...
On model selection consistency of M-estimators with geometrically decomposable penalties
model selection consistency M-estimators geometrically decomposable penalties
2013/6/14
Penalized M-estimators are used in many areas of science and engineering to fit models with some low-dimensional structure in high-dimensional settings. In many problems arising in bioinformatics, sig...
Model Selection for High-Dimensional Regression under the Generalized Irrepresentability Condition
Model Selection High-Dimensional Regression Generalized Irrepresentability Condition
2013/6/13
In the high-dimensional regression model a response variable is linearly related to $p$ covariates, but the sample size $n$ is smaller than $p$. We assume that only a small subset of covariates is `ac...
IMAGE ACQUISITION AND MODEL SELECTION FOR MULTI-VIEW STEREO
Photogrammetry, Surface Reconstruction, Multi-View Stereo, Dense Image Matching, Structure from Motion
2014/4/25
Dense image matching methods enable efficient 3D data acquisition. Digital cameras are available at high resolution, high geometric
and radiometric quality and high image repetition rate. They can b...
Group-Sparse Model Selection: Hardness and Relaxations
Signal Approximation Structured Sparsity Interpretability Tractability Dynamic Programming Compressive Sensing
2013/5/2
Group-based sparsity models are proven instrumental in linear regression problems for recovering signals from much fewer measurements than standard compressive sensing. The main promise of these model...
Model selection and clustering in stochastic block models with the exact integrated complete data likelihood
Random graphs stochastic block models integrated classication likelihood
2013/4/27
The stochastic block model (SBM) is a mixture model used for the clustering of nodes in networks. It has now been employed for more than a decade to analyze very different types of networks in many sc...
Model selection and estimation of a component in additive regression
Model selection estimation component additive regression
2012/11/23
Let $Y\in\R^n$ be a random vector with mean $s$ and covariance matrix $\sigma^2P_n\tra{P_n}$ where $P_n$ is some known $n\times n$-matrix. We construct a statistical procedure to estimate $s$ as well ...
Testing in the Presence of Nuisance Parameters: Some Comments on Tests Post-Model-Selection and Random Critical Values
Nuisance Parameters Post-Model-Selection Random Critical Values
2012/11/22
We point out that the ideas underlying some test procedures recently proposed for testing post-model-selection (and for some other test problems) in the econometrics literature have been around for qu...
Simultaneous Model Selection and Estimation for Mean and Association Structures with Clustered Binary Data
association clustered binary data generalized estimating equation logistic regression variable selection
2012/9/17
This paper investigates the property of the penalized estimating equations when both the mean and association structures are modelled. To select variables for the mean and association structures seque...