Results 1  10
of
19
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 45 (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
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 20 (7 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.
TREK SEPARATION FOR GAUSSIAN GRAPHICAL MODELS
 SUBMITTED TO THE ANNALS OF STATISTICS
, 2009
"... 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

Cited by 11 (5 self)
 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.
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 8 (8 self)
 Add to MetaCart
(Show Context)
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.
A probability index of the robustness of a causal inference
 Journal of Educational and Behavioral Statistics
, 2004
"... ..."
(Show Context)
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
(Show Context)
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.
Direct correspondence to
"... Education, and of Fisheries and Wildlife. He can be contacted at room 460 Erickson Hall, ..."
Abstract
 Add to MetaCart
(Show Context)
Education, and of Fisheries and Wildlife. He can be contacted at room 460 Erickson Hall,
errors using dseparation
, 2002
"... Running Head: dsep test for path models with correlated errors ..."
Robust Statistics for Describing Causality in Multivariate Time Series.
"... A widely agreed upon definition of time series causality inference, established in the seminal 1969 article of Clive Granger (1969), is based on the relative ability of the history of one time series to predict the current state of another, conditional on all other past information. While the Grang ..."
Abstract
 Add to MetaCart
(Show Context)
A widely agreed upon definition of time series causality inference, established in the seminal 1969 article of Clive Granger (1969), is based on the relative ability of the history of one time series to predict the current state of another, conditional on all other past information. While the Granger Causality (GC) principle remains uncontested, its literal application is challenged by practical and physical limitations of the process of discretely sampling continuous dynamic systems. Advances in methodology for timeseries causality subsequently evolved mainly in econometrics and brain imaging: while each domain has specific data and noise characteristics the basic aims and challenges are similar. Dynamic interactions may occur at higher temporal or spatial resolution than our ability to measure them, which leads to the potentially false inference of causation where only correlation is present. Causality assignment can be seen as the principled partition of spectral coherence among interacting signals using both autoregressive (AR) modeling and spectral decomposition. While both approaches are theoretically equivalent, interchangeably describing linear dynamic processes, the purely spectral approach currently differs in its somewhat
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 Stat ..."
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.