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Mann-Whitney Test with Adjustments to Pre-treatment Variables for Missing Values and Observational Study
Dimension reduction Kernel smoothing Mann-Whitney statistic Missing out- comes Observational studies Selection bias
2016/1/25
The conventional Wilcoxon/Mann-Whitney test can be invalid for comparing treatment effects in the presence of missing values or in observational studies. This is because the missingness of the outcome...
Mann-Whitney Test with Adjustments to Pre-treatment Variables for Missing Values and Observational Study
Dimension reduction Kernel smoothing Mann-Whitney statistic
2016/1/20
The conventional Wilcoxon/Mann-Whitney test can be invalid for comparing treatment effects in the presence of missing values or in observational studies. This is because the missingness of the outcome...
Identifiability of causal effects for binary variables with data missing due to death
Causal inference Missing due to death Identifiability EM algorithm Bounds
2016/1/19
Summary: We discuss evaluation of causal effects with missing data due to death. Frangakis et al. (2007)proposed an approach for estimating the causal effects of interest under some assumptions. In th...
Saddlepoint Approximation for Moments of Random Variables
Saddlepoint Approximation Higher moments Sums of i.i.d.ran- dom variables
2016/1/19
In this paper we introduce a saddlepoint approximation method for higher-order moments like E(S − a) m+ ,a > 0, where the random variable S in these expectations could be a single random variabl...
A command for estimating spatial-autoregressive models with spatial-autoregressive disturbances and additional endogenous variables
spivreg spatial-autoregressive models
2015/9/24
We describe the spivreg command, which estimates the parameters
of linear cross-sectional spatial-autoregressive models with spatial-autoregressive
disturbances, where the model may also contain add...
Distribution and Symmetric Distribution Regression Model for Histogram-Valued Variables
data with variability linear regression Symbolic Data Analysis quantile functions Mallows distance
2013/4/28
Histogram-valued variables are a particular kind of variables studied in Symbolic Data Analysis where to each entity under analysis corresponds a distribution that may be represented by a histogram or...
Respondent privacy and estimation efficiency in randomized response surveys for discrete-valued sensitive variables
Jeopardy measure numerical stigmatizing variable revealing probability
2013/4/28
In some socio-economic surveys, data are collected on sensitive or stigmatizing issues such as tax evasion, criminal conviction, drug use, etc. In such surveys, direct questioning of respondents is no...
Ensembling Classification Models Based on Phalanxes of Variables with Applications in Drug Discovery
classification ranking ensemble random forest cluster pha-lanx
2013/4/28
We have proposed an ensemble method which aggregates over clusters of predictor variables. We form the clusters (we call phalanxes) by joining variables together. The variables in a phalanx are good t...
Generalized Sobol sensitivity indices for dependent variables: numerical methods
Dependent variables Extended basis Greedy algorithm LARS Sensitivity analysis Sobol decomposition
2013/4/28
The hierarchically orthogonal functional decomposition of any measurable function f of a random vector X=(X_1,...,X_p) consists in decomposing f(X) into a sum of increasing dimension functions dependi...
Correlated variables in regression: clustering and sparse estimation
Canonical correlation group Lasso Hierarchical clustering High-dimensional inference Lasso Oracle inequality Variable screening Variable selection
2012/11/23
We consider estimation in a high-dimensional linear model with strongly correlated variables. We propose to cluster the variables first and do subsequent sparse estimation such as the Lasso for cluste...
On nonparametric inference for $P(Xaired variables
nonparametric inference $P(X paired variables
2012/11/22
We propose a class of nonparametric point estimators for $\theta=P(Xase where $(X,Y)$ are paired, possibly dependent, continuous random variables. We make use of the pairing structure fo...
The Graphical Identification for Total Effects by using Surrogate Variables
Graphical Identification Total Effects Surrogate Variables
2012/9/19
Consider the case where cause-effect relation-ships between variables can be described as a directed acyclic graph and the corresponding linear structural equation model. This paper provides graphical...
Towards Characterizing Markov Equivalence Classes for Directed Acyclic Graphs with Latent Variables
DAG maximal ancestral graph Markov equivalence
2012/9/18
It is well known that there may be many causal explanations that are consistent with a given set of data. Recent work has been done to represent the common aspects of these explanations into one repre...
Mixing Coefficients Between Discrete and Real Random Variables: Computation and Properties
Mixing Coefficients Between Discrete Real Random Variables Computation Properties
2012/9/17
In this paper we study the problem of estimating the mixing coefficients between two random vari-ables. Three different mixing coefficients are studied,namely alpha-mixing, beta-mixing and phi-mixing ...
Learning LiNGAM based on data with more variables than observations
LiNGAM based variables observations
2012/9/17
A very important topic in systems biology is developing statistical methods that automatically find causal relations in gene regulatory net-works with no prior knowledge of causal connectivity. Many m...