VariableSelection相关论文
High-dimensional data encountered in genomic and proteomic studies are often limited by the sample size but has a higher......
The computational speed in the feature selection of Mahalanobis-Taguchi system (MTS) using standard binary particle swar......
Traditional Chinese Medicine(TCM)study typically has both continuous and categorical covariates,especially for the clini......
A novel method to quickly identify polymer bonded explosives by a combination of infrared spectrosco
The fast recognition of explosives is of great importance in national defense and security fields and is also an enormou......
Combining the Suitability Score and Successive Projection Algorithm for Variable Selection in Partia
Infrared spectroscopy(IR)technique combined with multivariate statistical analysis methods is widely used in fundamental......
Near infrared(NIR)spectroscopy combined with multivariate calibration has been widely used for the quantitative analysis......
Variable selection from the high-dimensional survival data is a fundamental and challenging approach in recent years....
We investigate the scenario of selecting variables in both the group level and within-group level simultaneously,in the ......
In high-dimensional regression analysis where the number of potential covariates is much larger than the sample size,inf......
We propose a Random Splitting Model Averaging procedure,RSMA,to achieve stable predictions in high-dimensional linear mo......
Quantile regression for partially linear varying-coefficient model with censoring indicators missing
In this paper,we focus on the partially linear varying-coefficient quantile regression model when the data are right cen......
Penalized empirical likelihood inference for sparse additive hazards regression with a divergent num
High-dimensional sparse modelling with censored survival data is of great practical importance,as exemplified by applica......
In this talk,I will discuss a nonlinear mixed-effects scalar-on-function regression model using a Gaussian process prior......
Its theoretically desirable to perform variable selection via penalized likelihood that directly penalizes the number of......
In applied statistics the main interest in many applications in science is explaining the relationships between multiple......
In this paper,we study a nonparametric approach regarding a general nonlinear reduced form equation to achieve a better ......
Over the last decade,Electronic Health Records(EHRs)systems have been increasingly implemented at US hospitals.Huge amou......
We study variable selection problem in causal inference.This is different from the regular variable selection problem su......
Modern biological techniques have led to a variety of types of data,which are often used to identify important biomarker......
This talk gives an overview of the Q-matrix based diagnostic classification models and the associated statistical infere......
Nonignorable nonresponse presents a great challenge in statistical analysis since the nonresponse mechanism/propensity d......
Estimate Variable Importance for Recurrent Event Outcomes with an Application to Identify Hypoglycem
Recurrent event data are important data type for medical research.In particular,many safety endpoints are recurrent outc......
SPOT:Sparse Optimal Transformations for High Dimensional Variable Selection and Exploratory Regressi
We develop a novel method called SParse Optimal Transformations(SPOT)to simultaneously select important variables and ex......
Causal inference practitioners are routinely presented with the challenge of wanting to adjust for large numbers of cova......
Regularized variable selection is a powerful tool for identifying the true regression model from a pool of candidates by......
LOGIT TREE MODELS FOR DISCRETE CHOICE DATA WITH APPLICATION TO ADVICE-SEEKING PREFERENCES AMONG CHIN
Logit models are popular tools for analyzing discrete choice and ranking data.The models assume that judges rate each it......
The regularization approach for variable selection was well developed for a completely observed data set in the past two......
M-estimation is a widely used technique for robust statistical inference.In this paper,we study the asymptotic propertie......
Weighted LAD-LASSO method for robust parameter estimation and variable selection for Partially Linea
We study robust parameter estimation and variable selection for partially linear models when the dimension of covariates......
Geographical classification of Nanfeng mandarin by near infrared spectroscopy coupled with chemometr
Near infrared spectroscopy (NIRS), coupled with principal component analysis and wavelength selection techniques, has be......
The relevance study of effective information between near infrared spectroscopy and chondroitin sulf
Near infrared spectroscopy (NIRS) is based on molecular overtone and combination vibrations. It is difficult to assign s......
Wavelet-based classification and influence matrix analysis method for the fast discrimination of Chi
A discriminant analysis technique using wavelet transformation (WT) and influence matrix analysis (CAIMAN) method is pro......
Characteristic wavelength selection of volatile organic compounds infrared spectra based on improved
As important components of air pollutant, volatile organic compounds (VOCs) can cause great harm to environment and huma......
为实现苹果可溶性固形物(SSC)的便携式快速检测, 利用环形光纤探头和微型光谱仪搭建便携式苹果可溶性固形物光谱采集系统, 结合无......
Three-step hybrid strategy towards efficiently selecting variables in multivariate calibration of ne
Variable(feature or wavelength)selection [1] is a critical step in multivariate calibration of near-infrared(NIR)spe......
With increasing development in innovative instrumentation,the chemical data is usually in the shape of high dimensio......
The Model Adaptive Space Shrinkage(MASS)Approacha new method for Simultaneous Variable Selection and
Variable selection and outlier detection are of important process in chemical modeling [1],but many scientists focus......
Toward better QSARQSPR modeling simultaneous outlier detection and variable selection using distribu
...
New Insights to Enhance the Reliability of Deep Learning Model for Spectral Analysis using Mean Impa
Deep leaning(DP)represents a novel branch of machine learning algorithms that attempt to model highlevel abstraction......
研究方法对任一学科发展均具有基础性影响。伴随比较政治学对其他学科研究方法的吸收借鉴和自身发展，在学者应用过程中，无论是理论构......
