Mathematical Statistics

By R. F.. 1944

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Citations in the corpus

Quantile tomography: using quantiles with multivariate data (2008, methodology): “…to an empirical distribution indeed means some statistical advance, in the spirit of the principle of the approach called “näıve statistics” (Hájek and Vorĺıčková, 1977) or “analogy” (Goldberger, 1968; Manski, 1988) or “plug-in” (Efron and Tibshirani, 1993): the result of the…”
On the distribution of wildfire sizes (2003, methodology): “Standard errors (SEs) of the parameter estimates given in parentheses in Table 1 were obtained via the diagonal of the inverse of the Hessian of the log-likelihood function (for details see e.g. Chapter 13.8 of Wilks, 1962).”
ESTIMATING COMMUNITY STABILITY AND ECOLOGICAL INTERACTIONS FROM TIME‐SERIES DATA (2003, methodology): “The technique can be adapted for CLS estimation using various chi-square statistics that arise under this type of estimation (Knight 2000).”
A nonparametric estimate of a multivariate density function (1965, unknown): “Wald (1943), Tukey (1947), or Wilks (1962)].”
Pricing Default Swaps: Empirical Evidence (2003, methodology): “As to the choice of the optimal degree of the integrated hazard function, the paired Z-tests indicate that the quadratic model works significantly better than, or not significantly different from, the linear and cubic models.”
A Divergence Statistic for Industrial Localization (2005, background): “…1)-vector of probability estimates, p̂i p̂ir Nirni : r 1, . . . , R 1 (19) converges in law to an (R 1)-variate normal distribution [see, for example, Wilks (1962)], and in particular that 19 It should be noted that Ellison and Glaeser (1997) do calculate the variance of G under the null…”
Size-Estimation Framework with Applications to Transitive Closure and Reachability (1997, background): “We review some basic notions from the theory of statistical inference (see, e.g., Kiefer [17] or Bickel and Doksum [2]) in the context of our framework.”
Information geometry and sufficient statistics (2012, background): “(cf. [4, (2.17)], [9, Theorem 1, p. 117]) Assume that ( M, Ω 1 , µ 1 , p 1 ) is a k -integrable parametrized measure model and Ω 2 is a measurable space.”