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Analysis of Messy Data
01 Jan 1992-
About: The article was published on 1992-01-01 and is currently open access. It has received 1741 citations till now. The article focuses on the topics: Stratified sampling & One-way analysis of variance.
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01 Jan 2005
TL;DR: This chapter starts with the simplest replicated designs and progresses through experiments with two or more groups, direct designs, factorial designs and time course experiments with technical as well as biological replication.
Abstract: A survey is given of differential expression analyses using the linear modeling features of the limma package. The chapter starts with the simplest replicated designs and progresses through experiments with two or more groups, direct designs, factorial designs and time course experiments. Experiments with technical as well as biological replication are considered. Empirical Bayes test statistics are explained. The use of quality weights, adaptive background correction and control spots in conjunction with linear modelling is illustrated on the β7 data.
5,920 citations
TL;DR: Both linear regressions and multivariate analyses correlating three global neuropsychological tests with a number of structural and neurochemical measurements performed on a prospective series of patients with Alzheimer's disease and 9 neuropathologically normal subjects reveal very powerful correlations with all three psychological assays.
Abstract: We present here both linear regressions and multivariate analyses correlating three global neuropsychological tests with a number of structural and neurochemical measurements performed on a prospective series of 15 patients with Alzheimer's disease and 9 neuropathologically normal subjects. The statistical data show only weak correlations between psychometric indices and plaques and tangles, but the density of neocortical synapses measured by a new immunocytochemical/densitometric technique reveals very powerful correlations with all three psychological assays. Multivariate analysis by stepwise regression produced a model including midfrontal and inferior parietal synapse density, plus inferior parietal plaque counts with a correlation coefficient of 0.96 for Mattis's Dementia Rating Scale. Plaque density contributed only 26% of that strength.
4,020 citations
TL;DR: This procedure implements random effects in the statistical model and permits modeling the covariance structure of the data, and can compute efficient estimates of fixed effects and valid standard errors of the estimates in the SAS System.
Abstract: Mixed linear models were developed by animal breeders to evaluate genetic potential of bulls. Application of mixed models has recently spread to all areas of research, spurred by availability of advanced computer software. Previously, mixed model analyses were implemented by adapting fixed-effect methods to models with random effects. This imposed limitations on applicability because the covariance structure was not modeled. This is the case with PROC GLM in the SAS® System. Recent versions of the SAS System include PROC MIXED. This procedure implements random effects in the statistical model and permits modeling the covariance structure of the data. Thereby, PROC MIXED can compute efficient estimates of fixed effects and valid standard errors of the estimates. Modeling the covariance structure is especially important for analysis of repeated measures data because measurements taken close in time are potentially more highly correlated than those taken far apart in time.
2,770 citations
Cites methods from "Analysis of Messy Data"
...For more information on mixed models, see Searle (1971), Harville (1977), McLean et al. (1991), Milliken and Johnson (1992), and Searle et al. (1992)....
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TL;DR: It is concluded that VR1 is required for inflammatory sensitization to noxious thermal stimuli but also that alternative mechanisms are sufficient for normal sensation of noxious heat.
Abstract: The vanilloid receptor-1 (VR1) is a ligand-gated, non-selective cation channel expressed predominantly by sensory neurons. VR1 responds to noxious stimuli including capsaicin, the pungent component of chilli peppers, heat and extracellular acidification, and it is able to integrate simultaneous exposure to these stimuli. These findings and research linking capsaicin with nociceptive behaviours (that is, responses to painful stimuli in animals have led to VR1 being considered as important for pain sensation. Here we have disrupted the mouse VR1 gene using standard gene targeting techniques. Small diameter dorsal root ganglion neurons isolated from VR1-null mice lacked many of the capsaicin-, acid- and heat-gated responses that have been previously well characterized in small diameter dorsal root ganglion neurons from various species. Furthermore, although the VR1-null mice appeared normal in a wide range of behavioural tests, including responses to acute noxious thermal stimuli, their ability to develop carrageenan-induced thermal hyperalgesia was completely absent. We conclude that VR1 is required for inflammatory sensitization to noxious thermal stimuli but also that alternative mechanisms are sufficient for normal sensation of noxious heat.
1,700 citations
Cites methods from "Analysis of Messy Data"
...In both cases, follow up analysis was carried out using Scheffes test, where appropriat...
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TL;DR: A preliminary set of research guidelines aimed at stimulating discussion among software researchers, intended to assist researchers, reviewers, and meta-analysts in designing, conducting, and evaluating empirical studies.
Abstract: Empirical software engineering research needs research guidelines to improve the research and reporting processes. We propose a preliminary set of research guidelines aimed at stimulating discussion among software researchers. They are based on a review of research guidelines developed for medical researchers and on our own experience in doing and reviewing software engineering research. The guidelines are intended to assist researchers, reviewers, and meta-analysts in designing, conducting, and evaluating empirical studies. Editorial boards of software engineering journals may wish to use our recommendations as a basis for developing guidelines for reviewers and for framing policies for dealing with the design, data collection, and analysis and reporting of empirical studies.
1,541 citations
Cites background from "Analysis of Messy Data"
...Cross-over designs with blocking are more difficult to analyze correctly than researchers may expect (see [33], chapter 32)....
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References
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TL;DR: In this article, a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic is presented, which does not depend on a formal model of the structure of the heteroSkewedness.
Abstract: This paper presents a parameter covariance matrix estimator which is consistent even when the disturbances of a linear regression model are heteroskedastic. This estimator does not depend on a formal model of the structure of the heteroskedasticity. By comparing the elements of the new estimator to those of the usual covariance estimator, one obtains a direct test for heteroskedasticity, since in the absence of heteroskedasticity, the two estimators will be approximately equal, but will generally diverge otherwise. The test has an appealing least squares interpretation.
25,689 citations
TL;DR: In this article, Lindley et al. make the less restrictive assumption that such a normal, homoscedastic, linear model is appropriate after some suitable transformation has been applied to the y's.
Abstract: [Read at a RESEARCH METHODS MEETING of the SOCIETY, April 8th, 1964, Professor D. V. LINDLEY in the Chair] SUMMARY In the analysis of data it is often assumed that observations Yl, Y2, *-, Yn are independently normally distributed with constant variance and with expectations specified by a model linear in a set of parameters 0. In this paper we make the less restrictive assumption that such a normal, homoscedastic, linear model is appropriate after some suitable transformation has been applied to the y's. Inferences about the transformation and about the parameters of the linear model are made by computing the likelihood function and the relevant posterior distribution. The contributions of normality, homoscedasticity and additivity to the transformation are separated. The relation of the present methods to earlier procedures for finding transformations is discussed. The methods are illustrated with examples.
12,158 citations
419 citations
TL;DR: In this paper, the distribution of the characteristic roots of the sample covariance matrix is studied for multivariate populations with finite fourth cumulants, and the asymptotic forms of the marginal and joint distributions are given.
Abstract: SUMMARY The distribution of the characteristic roots of the sample covariance matrix is studied for multivariate populations with finite fourth cumulants. For large sample sizes, the asymptotic forms of the marginal and joint distributions are given. For moderate and small sample sizes, empirical sampling techniques are used: various trivariate models are simulated, and the distributions of the sample roots are empirically observed. It is shown that the distributional results for a multivariate normal population are nonrobust to departures from normality; they are mainly affected by nonzero fourth cumulants and cross-cumulants of the parent population.
139 citations