Results 1 
9 of
9
Revising Regulatory Networks: From Expression Data to Linear Causal Models
 Journal of Biomedical Informatics
, 2002
"... Discovering the complex regulatory networks that govern mRNA expression is an important but dicult problem. Many current approaches use only expression data from microarrays to infer the likely network structure. However, this ignores much existing knowledge because for a given organism and syste ..."
Abstract

Cited by 13 (5 self)
 Add to MetaCart
Discovering the complex regulatory networks that govern mRNA expression is an important but dicult problem. Many current approaches use only expression data from microarrays to infer the likely network structure. However, this ignores much existing knowledge because for a given organism and system under study, a biologist may already have a partial model of gene regulation. We propose a method for revising and improving these initial models, which may be incomplete or partially incorrect, with expression data. We demonstrate our approach by revising a model of photosynthesis regulation proposed by a biologist for Cyanobacteria. Applied to wild type expression data, our system suggested several modi cations consistent with biological knowledge.
Statistical methods in psychology journals: guidelines and explanations
 American Psychologist
, 1999
"... In the light of continuing debate over the applications of significance testing in psychology journals and following the publication of Cohen (1994), the Board of Scientific Affairs (BSA) of the APA convened a committee called the Task Force on Statistical Inference (TFSI) whose charge was “to eluci ..."
Abstract

Cited by 9 (0 self)
 Add to MetaCart
In the light of continuing debate over the applications of significance testing in psychology journals and following the publication of Cohen (1994), the Board of Scientific Affairs (BSA) of the APA convened a committee called the Task Force on Statistical Inference (TFSI) whose charge was “to elucidate some of the controversial issues surrounding applications
Integrating experimental and observational personality research – the contributions of Hans Eysenck
 Journal of Personality
, 2008
"... A fundamental aspect of Hans Eysenck’s research was his emphasis upon using all the tools available to the researcher to study personality. This included correlational, experimental, physiological, and genetic approaches. 50 years after Cronbach’s call for the reunification of the two disciplines of ..."
Abstract

Cited by 5 (5 self)
 Add to MetaCart
A fundamental aspect of Hans Eysenck’s research was his emphasis upon using all the tools available to the researcher to study personality. This included correlational, experimental, physiological, and genetic approaches. 50 years after Cronbach’s call for the reunification of the two disciplines of psychology (Cronbach, 1957) and 40 years after Eysenck’s plea for experimental approaches to personality research (H. J. Eysenck, 1966), what is the status of the unification? Should personality researchers use experimental techniques? Do experimental techniques allow us to tease out causality, and are we communicating the advantages of combining experimental with multivariate correlational techniques? We review the progress made since Cronbach and Eysenck’s original papers and suggest that although it is still uncommon to find experimental studies of personality, psychology would benefit from the joint use of correlational and experimental approaches.
Using Markov Blankets for Causal Structure Learning
"... We show how a generic featureselection algorithm returning strongly relevant variables can be turned into a causal structurelearning algorithm. We prove this under the Faithfulness assumption for the data distribution. In a causal graph, the strongly relevant variables for a node X are its parents ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
We show how a generic featureselection algorithm returning strongly relevant variables can be turned into a causal structurelearning algorithm. We prove this under the Faithfulness assumption for the data distribution. In a causal graph, the strongly relevant variables for a node X are its parents, children, and children’s parents (or spouses), also known as the Markov blanket of X. Identifying the spouses leads to the detection of the Vstructure patterns and thus to causal orientations. Repeating the task for all variables yields a valid partially oriented causal graph. We first show an efficient way to identify the spouse links. We then perform several experiments in the continuous domain using the Recursive Feature Elimination featureselection algorithm with Support Vector Regression and empirically verify the intuition of this direct (but computationally expensive) approach. Within the same framework, we then devise a fast and consistent algorithm, Total Conditioning (TC), and a variant, TCbw, with an explicit backward featureselection heuristics, for Gaussian data. After running a series of comparative experiments on five artificial networks, we argue that Markov blanket algorithms such as TC/TCbw or GrowShrink scale better than the reference PC algorithm and provides higher structural accuracy.
A Probability Index of the Robustness of a Causal Inference
"... Causal inference is an important, controversial topic in the social sciences, where it is difficult to conduct experiments or measure and control for all confounding variables. To address this concern, the present study presents a probability index to assess the robustness of a causal inference to t ..."
Abstract

Cited by 2 (1 self)
 Add to MetaCart
Causal inference is an important, controversial topic in the social sciences, where it is difficult to conduct experiments or measure and control for all confounding variables. To address this concern, the present study presents a probability index to assess the robustness of a causal inference to the impact of a confounding variable. The information from existing covariates is used to develop a reference distribution for gauging the likelihood of observing a given value of the impact of a confounding variable. Applications are illustrated with an empirical example pertaining to educational attainment. The methodology discussed in this study allows for multiple partial causes in the complex social phenomena that we study, and informs the controversy about causal inference that arises from the use of statistical models in the social sciences.
Revising Regulatory Networks: From Expression Data
"... Discovering the complex regulatory networks that govern mRNA expression is an important but dicult problem. Many current approaches use only expression data from microarrays to infer the likely network structure. However, this ignores much existing knowledge because for a given organism and syste ..."
Abstract
 Add to MetaCart
Discovering the complex regulatory networks that govern mRNA expression is an important but dicult problem. Many current approaches use only expression data from microarrays to infer the likely network structure. However, this ignores much existing knowledge because for a given organism and system under study, a biologist may already have a partial model of gene regulation. We propose a method for revising and improving these initial models, which may be incomplete or partially incorrect, with expression data. We demonstrate our approach by revising a model of photosynthesis regulation proposed by a biologist for Cyanobacteria. Applied to wild type expression data, our system suggested several modi cations consistent with biological knowledge.
Submitted to the Annals of Statistics TREK SEPARATION FOR GAUSSIAN GRAPHICAL MODELS
, 812
"... Gaussian graphical models are semialgebraic subsets of the cone of positive definite covariance matrices. Submatrices with low rank correspond to generalizations of conditional independence constraints on collections of random variables. We give a precise graphtheoretic characterization of when su ..."
Abstract
 Add to MetaCart
Gaussian graphical models are semialgebraic subsets of the cone of positive definite covariance matrices. Submatrices with low rank correspond to generalizations of conditional independence constraints on collections of random variables. We give a precise graphtheoretic characterization of when submatrices of the covariance matrix have small rank for a general class of mixed graphs that includes directed acyclic and undirected graphs as special cases. Our new trek separation criterion generalizes the familiar dseparation criterion. Proofs are based on the trek rule, the resulting matrix factorizations, and classical theorems of algebraic combinatorics on the expansions of determinants of path polynomials.
Statistical Methods in Psychology Journals Guidelines and Explanations
"... n the light of continuing debate over the applications of significance testing in psychology journals and following the publication of Cohen's (1994) article, the Board of Scientific Affairs (BSA) of the American Psychological Association (APA) convened a committee called the Task Force on Statistic ..."
Abstract
 Add to MetaCart
n the light of continuing debate over the applications of significance testing in psychology journals and following the publication of Cohen's (1994) article, the Board of Scientific Affairs (BSA) of the American Psychological Association (APA) convened a committee called the Task Force on Statistical Inference (TFSI) whose charge was "to elucidate some of the controversial issues surrounding applications of statistics including significance testing and its alternatives; alternative underlying models and data transformation; and newer methods made possible by powerful computers " (BSA, personal communication, February 28, 1996). Robert Rosenthal, Robert Abelson, and Jacob Cohen (cochairs) met initially and agreed on the desirability of having several types of specialists on the task force: statisticians, teachers of statistics, journal editors, authors of statistics books, computer experts, and wise elders. Nine individuals were subsequently invited to join and all agreed.
For personal use onlynot for distribution. Statistical Methods in Psychology Journals Guidelines and Explanations
"... In the light of continuing debate over the applications of significance testing in psychology journals and following the publication of Cohen's (1994) article, the Board of Scientific Affairs (BSA) of the American Psychological Association (APA) convened a committee called the Task Force on Statisti ..."
Abstract
 Add to MetaCart
In the light of continuing debate over the applications of significance testing in psychology journals and following the publication of Cohen's (1994) article, the Board of Scientific Affairs (BSA) of the American Psychological Association (APA) convened a committee called the Task Force on Statistical Inference (TFSI) whose charge was "to elucidate some of the controversial issues surrounding applications of statistics including significance testing and its alternatives; alternative underlying models and data transformation; and newer methods made possible by powerful computers " (BSA, personal communication, February 28, 1996). Robert Rosenthal, Robert Abelson, and Jacob Cohen (cochairs) met initially and agreed on the desirability of having several types of specialists on the task force: statisticians, teachers of statistics, journal editors, authors of statistics books, computer experts, and wise elders. Nineindividuals were subsequently invited to join and all agreed. These were Leona Aiken, Mark