Statistical Inference

By Guillaume Flandin. 2012

3,346 citations recorded by the source corpus.

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Citations in the corpus

Meta-Learning Probabilistic Inference for Prediction (2018, background): “So, if the approximate posterior q φ ( ψ | D ) is rich enough, global optimization will recover the true posterior p ( ψ | D ) (assuming p ( ψ | D ) obeys identiﬁability conditions (Casella and Berger, 2002)).”
Auditing black-box models for indirect influence (2016, background): “Specifically, we show that if the feature W being obscured has Gaussian conditionals, then our obscuring procedure will create a data set on which the F-test [6] will fail, which means that the null hypothesis (that the conditionals are now identical) will not be invalidated.”
Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose (2013, methodology): “Next, we use maximum-likelihood estimation (Casella and Berger 2001) to estimate the parameters of the Gaussian distribution from which these points originate, ˆ µ = 100 i =1 x i , and ˆ σ = 100 i =1 ( x i − ˆ µ ) 2 .”
Non-Asymptotic Analysis of Stochastic Approximation Algorithms for Machine Learning (2011, background): “, [27]), and t hus he leading term is asymptotically optimal.”
Generalized sensitivities and optimal experimental design (2010, neutral): “, (see [14, 16, 22, 24, 33]).”
Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation (2015, background): “If a population follows a normal distribution, then the sampling distribution of a sample mean calculated from independent observations will be normally distributed for any sample size, because the sum of two independent normal random variables is also normally distributed [10].”
Variational learning for latent Gaussian model of discrete data (2012, methodology): “First-order delta method The Delta method is used to approximate moments of a function using the Taylor expansion [Casella and Berger, 2001].”
Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies (2015, methodology): “Side-by-side boxplots [40] and empirical cumulative distribution functions (CDFs) [41] can be used to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample.”
(De)marketing to Manage Consumer Quality Inferences (2013, background): “Given marketing effort a, late consumers know that the probability density function of first-period sales m is f(m|a) if quality is high and f(m/b|a)(1/b) if quality is low, following the Jacobian transformation (Casella and Berger 1990).”
Detection of Item Preknowledge Using Likelihood Ratio Test and Score Test (2017, background): “Note that both the null and alternative hypotheses in our case are composite; for composite hypotheses, the Neyman–Pearson lemma (e.g., Cox & Hinkley, 1974) does not guarantee that the LRT is the most powerful test (e.g., Casella & Berger, 2002, p. 391; Romero et al., 2015).”
Semiparametric doubly robust targeted double machine learning: a review (2022, methodology): “A classic benchmarking or lower bound result for smooth parametric models is the Cramer-Rao bound [Casella and Berger, 2001, van der Vaart, 2002].”
Optimizing linear counting queries under differential privacy (2009, result): “The theorem follows from an application of the GaussMarkov theorem [16] and extends a similar result from [11].”
Maximal Reliability and Composite Reliability: Examining Their Difference for Multicomponent Measuring Instruments Using Latent Variable Modeling (2016, methodology): “This nested model testing is readily carried out using the likelihood ratio theory in the setting considered in this article with normality and large samples (e.g., Casella & Berger, 2002).”
Parametric, bootstrap, and jackknife variance estimators for the k-Nearest Neighbors technique with illustrations using forest inventory and satellite image data (2011, background): “Thus, inference requires expression of the relationship between a population parameter and its estimate in probabilistic terms (Dawid, 1983) such as in the form of familiar 1−α confidence intervals.”
Statistical properties and different methods of estimation of Gompertz distribution with application (2018, methodology): “Methods of Estimation 3.1 Method of Maximum Likelihood The method of maximum likelihood is the most frequently used method of parameter estimation (Casella and Berger(1990)).”