Large Sample Methods in Statistics: An Introduction with Applications

By Pranab Kumar Sen, J. Singer. 1993

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning (2016, methodology): “See the work of Sen & Singer (1993, Theorem 2.3.10 with Proposition 2.3.10).”
On the Likelihood Ratio Test for the Number of Factors in Exploratory Factor Analysis (2007, background): “Ferguson, 1996 ; Sen & Singer, 1993 ); (b) the assumption of multivariate normality is violated ( Yuan et al, 2002 ); (c) at least one of the unique variances is zero in the population (ie, the population version of the Heywood case); and (d) at least one column(s) of the matrix of factor loadings ”
Safe Reinforcement Learning (2015, background): “where N (μ, σ(2)) denotes a normal distribution with mean μ and standard deviation σ, and d → denotes convergence in distribution (Sen and Singer, 1993).”
Maximum penalized likelihood estimation for skew-normal and skew-t distributions (2012, background): “This fact is intuitive on noticing that both `(θ) and `p(θ) are Op(n) and they differ by Q, which is Op(1), but it can also easily be proved formally, under the above regularity conditions, following essentially an argument similar to Theorem 5.2.2 of Sen & Singer (1993).”
Application of modified information criterion to multiple change point problems (2006, methodology): “By Kolmogorov maximal inequality (Sen and Singer, 1993), that is, P{max 1≤k≤n | k∑ i=1 (Xi − EXi)| ≥ } ≤ 1 2 n∑ k=1 var(Xk) if X1, . . . , Xn is a sequence of independent random variables with EX 2 i ∞ for i = 1, . . . , n.”