Statistical Methods for Research Workers

By M. Kendall. 1937

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Citations in the corpus

Fast Conditional Independence Test for Vector Variables with Large Sample Sizes (2018, background): “They are the main work horse of classical hypothesis testing (see Fisher [1925], Chapter 21), which underlies most experimental and many observational techniques of data analysis.”
The Econometrics of Randomized Experiments (2016, methodology): “The first analysis is based on Fisher’s (1925, 1935) work on exact p-values for sharp null hypotheses. See for recent discussions Rosenbaum (1992), Gail, Tian and Piantadosi (1988), and Imbens and Rubin (2015), and in economics Young (2016).”
The State of Applied Econometrics - Causality and Policy Evaluation (2016, neutral): “which go back to Fisher [1925, 1935], Neyman [1923/1990, 1935], clarify how the act of randomization allows for the testing for the presence of treatment effects and the unbiased estimation of average treatment effects. Traditionally these methods have not been used much in economics. However, recen”
Interactive Effect of Rhizotrophic Microorganisms on Growth, Yield, and Nutrient Uptake of Wheat (2005, methodology): “The data on growth, yield, and nutrient uptake were averaged across two years and analyzed statistically, and least significant difference for separation of means was calculated at P ≤ 0.05 (Fisher, 1958).”
A review of copula models for economic time series (2012, background): “, [58]) that if Yi is continuous, thenUi ∼ U (0, 1), and in this case a copula is any joint distribution function with U(0, 1) margins.”
Linking crystallographic model and data quality (2012, unknown): “S2) (11, 20), which provide warmmicroclimates [southerly aspects receive greater direct radiation and achieve higher maximum summer temperatures (21)].”
Why P Values Are Not a Useful Measure of Evidence in Statistical Significance Testing (2008, methodology): “This line of reasoning necessitates a weighing of the evidence between two rival hypotheses, a situation impossible in Fisherian significance tests, where there is only the null hypothesis. Fisher never saw the need for an alternative hypothesis, and vigorously opposed its later inclusion by Jerzy N”
Agnostic notes on regression adjustments to experimental data: Reexamining Freedman's critique (2012, background): “The usual rationale is that adjustment tends to improve precision if the sample is large enough and the covariates are correlated with the outcome; this argument, which assumes that the regression model is correct, stems from Fisher (1932) and is taught Received October 2011; revised July 2012.”
The Variance of Intraclass Correlations in Three- and Four-Level Models (2012, methodology): “The intraclass correlation was introduced by Fisher (1925), who offered an estimator of the intraclass correlation, derived its variance in balanced experiments, and discovered a variance stabilizing transformation.”
Closed-loop and activity-guided optogenetic control. (2015, unknown): “This can be categorized as a problem of experiment design—an area of inquiry dating back to the early days of statistics (Fisher, 1925).”
The Analysis and Interpretation of Multivariate Data for Social Scientists (2002, background): “An early and important idea in statistics, originating from Fisher (1932), showed that adjustment for influential covariates is important in the comparison of means of continuous outcome variables, D (contrasting, say, the mean outcome among exposed to the mean among unexposed), not only for the rem”
Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods (2013, unknown): “Fisher’s combined probability test Fisher’s combined probability test (26) is a classical meta-analysis method that combines P -values from multiple individual statistical tests.”
A Comparison of Three Methods of Estimation in the Context of Spatial Modeling (2009, methodology): “Gilks, 1996). All chains converged, as indicated by tests devised by Geweke (1992) and Heidelberger and Welch (1983).”
Estimation of Copula Models for Time Series of Possibly Different Length (2001, unknown): “Noting that the expectation of the scores are zero at the true parameter, and expanding the above expression for the variance yields equation (24).”
Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis (2015, background): “Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat Biotechnol 32, 1053-1058, (2014). 2 Buettner, F.”
Data Lifecycle Challenges in Production Machine Learning (2018, methodology): “It is also worth mentioning alerts that are based on statistics including homogeneity tests, analysis of variance (e.g., ANOVA [30, 31]), time series analysis, and change detection.”