Wang et al., 2011 - Google Patents
Empirical likelihood for quantile regression models with longitudinal dataWang et al., 2011
- Document ID
- 4693841908635392820
- Author
- Wang H
- Zhu Z
- Publication year
- Publication venue
- Journal of statistical planning and inference
External Links
Snippet
We develop two empirical likelihood-based inference procedures for longitudinal data under the framework of quantile regression. The proposed methods avoid estimating the unknown error density function and the intra-subject correlation involved in the asymptotic covariance …
- 238000000034 method 0 abstract description 13
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/18—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for functional genomics or proteomics, e.g. genotype-phenotype associations, linkage disequilibrium, population genetics, binding site identification, mutagenesis, genotyping or genome annotation, protein-protein interactions or protein-nucleic acid interactions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
- G06F19/708—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for data visualisation, e.g. molecular structure representations, graphics generation, display of maps or networks or other visual representations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/16—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for molecular structure, e.g. structure alignment, structural or functional relations, protein folding, domain topologies, drug targeting using structure data, involving two-dimensional or three-dimensional structures
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/70—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds
- G06F19/706—Chemoinformatics, i.e. data processing methods or systems for the retrieval, analysis, visualisation, or storage of physicochemical or structural data of chemical compounds for drug design with the emphasis on a therapeutic agent, e.g. ligand-biological target interactions, pharmacophore generation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wang et al. | Empirical likelihood for quantile regression models with longitudinal data | |
| Elashoff et al. | An approach to joint analysis of longitudinal measurements and competing risks failure time data | |
| Wang et al. | Confounder adjustment in multiple hypothesis testing | |
| Weber et al. | Applying meta-analytic-predictive priors with the R Bayesian evidence synthesis tools | |
| Chiou et al. | Fitting accelerated failure time models in routine survival analysis with R package aftgee | |
| Pan et al. | Regression analysis of additive hazards model with latent variables | |
| Sun et al. | Semiparametric time‐varying coefficients regression model for longitudinal data | |
| Zhang et al. | Checking the adequacy for a distortion errors-in-variables parametric regression model | |
| Ke et al. | Bayesian meta-analytic SEM: A one-stage approach to modeling between-studies heterogeneity in structural parameters | |
| Torabi | Likelihood inference in generalized linear mixed measurement error models | |
| He et al. | Additive mean residual life model with latent variables under right censoring | |
| CN106770155B (en) | A kind of substance content analysis method | |
| Zhao et al. | Covariate measurement error correction methods in mediation analysis with failure time data | |
| Lunn et al. | Markov chain Monte Carlo techniques for studying interoccasion and intersubject variability: application to pharmacokinetic data | |
| Habeck et al. | Bayesian estimation of Karplus parameters and torsion angles from three-bond scalar couplings constants | |
| Pan et al. | Joint analysis of mixed types of outcomes with latent variables | |
| Maceachern et al. | Importance link function estimation for Markov chain Monte Carlo methods | |
| Li et al. | Survival analysis with heterogeneous covariate measurement error | |
| Gregorich et al. | Prediction Modeling With Many Correlated and Zero‐Inflated Predictors: Assessing the Nonnegative Garrote Approach | |
| Chen et al. | Principal component analyses in anthropological genetics | |
| Rodríguez-Girondo et al. | Sequential double cross-validation for assessment of added predictive ability in high-dimensional omic applications | |
| Koner et al. | Power and Sample Size Calculation of Two‐Sample Projection‐Based Testing for Sparsely Observed Functional Data | |
| Ma et al. | Quantile regression modeling of latent trajectory features with longitudinal data | |
| Pantazis et al. | Performance of parametric survival models under non-random interval censoring: A simulation study | |
| Ferede et al. | A mixed-effects joint model with skew-t distribution for longitudinal and time-to-event data: A Bayesian approach |