On the Future of Data Analysis

By Reinhard Viertl. 2002

788 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9afea816cb782cee50dec0f9aaa260d046725cfb

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Citations in the corpus

Capital structure and its determinants in the UK - a decompositional analysis (2002, unknown): “As total debt is substantially less than 4 See Tukey (1962) for details. total liabilities, total capital is less than total assets.”
Milestones in the history of thematic cartography, statistica l graphics, and data visualization (2008, background): “Still under the influence of the formal and numerical zeitgeist from the mid-1930s on, data visualization began to rise from dormancy in the mid 1960s, spurred largely by three significant developments: • In the USA, John W. Tukey, in a landmark paper, “The Future of Data Analysis” [294], issued a c”
Life After NHST: How to Describe Your Data Without “p-ing” Everywhere (2015, background): “But as Tukey (1962) put it, NHST provides an exact answer to the wrong question (pp. 13–14).”
Skewness-Invariant Measures of Kurtosis (2011, background): “(9) T −1 λ (y) = sinh(− + sinh−1(y cosh )), where = tanh−1 λ is also necessarily a skewness parameter in the sense of van Zwet (1964). Jones and Pewsey (2009) introduced skew-symmetric families of distributions generated by sinh–arcsinh transformation of the random variable Z which follows a density”
Airtime transfers and mobile communications: Evidence in the aftermath of natural disasters (2016, methodology): “Column (4) winsorizes the dependent variable at the 99th percentile following Tukey (1962) by replacing all values greater than the 99th percentile (2500RWF) with values at the 99th percentile.”
New Technology and Digital Worlds: Analyzing Evidence of Equity in Access, Use, and Outcomes (2010, background): “In spite of these limitations, we offer this review in the spirit of American statistician John Tukey (1962), who declared that “far better an approximate answer to the right question, which is often vague, than an exact answer to the wrong question, which can always be made more precise” (p. 62 ).”
Hierarchical Stochastic Neighbor Embedding (2016, background): “Among its goals are the extraction of important variables, the detection of outliers or the identiﬁcation of underlying non-convex structures [Tuk62].”
Models and Statistical Inference: The Controversy between Fisher and Neyman–Pearson (2006, background): “judgement based upon abstract results about the properties of particular techniques, whether obtained by mathematical proofs or empirical sampling (Tukey [1962], p.”
Robustness in Data Analysis: Criteria and Methods (2001, background): “Now we do not discuss how to use them tackling statistical problems: only note that their solutions result in evaluating some of the DSCs, and final decision making essentially depends on their values (Aivazyan et al., 1989; Tukey, 1962).”
Beyond subjective and objective in statistics (2015, background): “Over 50 years ago, Tukey criticized an obsession with mathematical optimization problems concentrating on one-dimensional criteria and simple parametric models in the name of objectivity, and stated that ‘in data analysis we must look to a very heavy emphasis on judgment’ (Tukey, 1962).”
Models as Approximations II: A Model-Free Theory of Parametric Regression (2016, methodology): “roximations_II_F.tex date: July 9, 2019 arXiv:1612.03257v2 [math.ST] 6 Jul 2019 2 A. BUJA ET AL. \The hallmark of good science is that it uses models and ’theory’ but never believes them." (J.W. Tukey, 1962, citing Martin Wilk) 1. INTRODUCTION We develop in this second article a model-free theo”
Linear tSNE optimization for the Web (2018, background): “UNDERSTANDING how data points are arranged in a high-dimensional space plays a crucial role in exploratory data analysis [25].”
Robust machine learning by median-of-means: Theory and practice (2017, background): “The book [62] traces back the origins of robust statistics to the fundamental works of John Tukey [86, 87], Peter Huber [39, 40] and Frank Hampel [33, 35].”
When the music's over. Does music skill transfer to children's and young adolescents' cognitive and academic skills? A meta-analysis (2017, methodology): “6 This procedure was adopted to reduce the weight of potential outliers in the anal ysis (Lipsey & Wilson, 2001; Schmidt & Hunter, 2015, pp. 235-236 ; Tukey, 1962).”