Statistical Inference
By R. Ackermann. Encyclopedia of Social Network Analysis and Mining, 2003
2,757 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/21154102098270f7e68d262319f144588ebd2448
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Maarten de Rijke
- Katja Hofmann
- Masrour Zoghi
- Filip Radlinski
- Panayiota Constantinou
- Peng Ding
- Ilya Markov
- Chang Li
In Statistics and Probability
- Panayiota Constantinou
- Xinran Li
- Peng Ding
- Katja Hofmann
- Filip Radlinski
- Chang Li
- A. P. Dawid
- Daniel Wallach
Citations in the corpus
- How well do crop modeling groups predict wheat phenology, given calibration data from the target population? (2019, methodology): “We use the law of total variance (Casella and Berger, 1990): var( ) var varY E Y structure E Y structure (5) where Y is either days from sowing to BBCH30 or to BBCH5.”
- Rerandomization and regression adjustment (2019, methodology): “Freedman (2008c,d)’s analyses allowed for treatment effect heterogeneity, in contrast to the existing literature on ANCOVA which often assumed additive treatment effects (Fisher 1935; Kempthorne 1952; Cox and Reid 2000). Lin (2013) proposed a solution to Freedman’s critique by running the OLS of the”
- Generalized fiducial inference for normal linear mixed models (2012, neutral): “The Annals of Statistics 2012, Vol. 40, No. 4, 2102–2127 DOI: 10.1214/12-AOS1030 © Institute of Mathematical Statistics, 2012”
- Extended Conditional Independence and Applications in Causal Inference (2015, methodology): “With notation as above, T is a sufficient statistic for θ if the conditional distribution of the full data X , given the value of T ( X ), does not depend on the value of the parameter θ [Casella and Berger, 2001, p. 272].”
- Bayesian Hierarchical Models With Conjugate Full-Conditional Distributions for Dependent Data From the Natural Exponential Family (2017, neutral): “s given by, n ∏ i=1 K(αi,κi) ! exp α′w−κ′ψ(w) ; w ∈Rn. The inverse of the transform of (7) is given by w =V−1(Y− µ), and the Jacobian is given by |det(V−1)|. Then, by a change-of-variables (e.g., see Casella and Berger, 2002), we have that the pdf of Y is given by, det(V−1) n ∏ i=1 K(αi,κi) ! exp α′”