CONFIDENCE LIMITS FOR THE EXPECTED VALUE OF AN ARBITRARY BOUNDED RANDOM VARIABLE WITH A CONTINUOUS DISTRIBUTION FUNCTION

By Theodore W. Anderson. 1969

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Citations in the corpus

On the Testability of Identification in Some Nonparametric Models With Endogeneity (2012, background): “Tests for this purpose have been proposed, among others, by Anderson (1951), Gill and Lewbel (1992), Cragg and Donald (1993, 1997), Robin and Smith (2000), and Kleibergen and Paap (2006).”
Estimating means of bounded random variables by betting (2020, background): “Anderson [A69] These intervals refer to the confidence intervals due to Anderson [2] which take a unique approach by considering the entire sample cumulative distribution function, rather than just the mean and variance.”
On Non‐parametric Testing, the Uniform Behaviour of the t‐test, and Related Problems (2004, background): “One way is to invert the confidence interval for the mean proposed by Anderson (1967). An”
Improved Nonparametric Inference for the Mean of a Bounded Random Variable with Application to Poverty Measures (2005, methodology): “…g[x(j)] ⎤⎦+ β ¡g(b)− g[x(r+1)]¢ for r = [nβ] (5) E[g(X)] ≥ 1 n ⎡⎣n−s−1X j=1 g[x(j)] + (s+ 1) g[x(n−s)] ⎤⎦− γ ¡g[x(n−s)]− g(a)¢ for s = |nγ| 3 Generalization of the Anderson’s Confidence Interval Anderson (1969) provides a very inference method for the mean of a bounded continuous random variable.”
Universal Off-Policy Evaluation (2021, methodology): “To obtain the mean of F+, we use the property that the mean of a distribution is the area above the CDF on the positive x-axis minus the area below the CDF on the negative x-axis [3].”