Likelihood-based Inference
Likelihood based inference for correlated diffusions
... of likelihood based inference for correlated diffusion processes using Markov chain Monte Carlo (MCMC) ...marginal likelihood for the parameters based on these observations is generally ...
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Likelihood-Based Inference for Max-Stable Processes
... Likelihood-Based Inference for Max-Stable Processes ...so likelihood-based methods remain far from providing a complete and flexible framework for ...practical, ...
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Likelihood based inference for diffusion driven models
... out likelihood based inference for diffusion driven models, for example discretely observed multivariate diffusions, continuous time stochas- tic volatility models and counting process ...sampling ...
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"Empirical Likelihood-Based Inference in Conditional Moment Restriction Models"
... EMPIRICAL LIKELIHOOD-BASED INFERENCE IN CONDITIONAL MOMENT RESTRICTION MODELS YUICHI KITAMURA, GAUTAM TRIPATHI, AND HYUNGTAIK AHN ...empirical likelihood estimator (MELE) of Qin and Lawless ...
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Methods of likelihood based inference for constructing stochastic climate models
... understood likelihood based inference to estimate the ...the inference and will also prove useful in restricting the parameter space to give stable ...
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Improving the accuracy of likelihood based inference in meta analysis and meta regression
... While likelihood-based inference is attractive both in terms of limiting properties and of implementation, its application in random-effects meta-analysis may result in misleading conclusions, ...
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Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression
... While likelihood-based inference is attractive both in terms of limiting properties and of implementation, its application in random-effects meta-analysis may result in misleading conclusions, ...
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Likelihood-Based Inference in Multivariate Panel Cointegration Models
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Likelihood Based Inference and Diagnostics for Spatial Data Models
... often based on comparing a functional summary statistic of the data with its expectation under the ...derived based on the score test and score test approximations related to the point process residuals ...
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Automated Likelihood Based Inference for Stochastic Volatility Models
... to develop an efficient MCMC algorithm, however. Moreover, our method for approximating the likelihood function is different from theirs. Meyer et al. uses a Kalman filter approach, where a sequence of ...
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Empirical likelihood based inference under complex sampling designs
... empirical likelihood approach is a design-based ...empirical likelihood approach proposed is different from the pseudoempirical likelihood approach ...
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Likelihood-based inference for a class of multivariate diffusions with unobserved paths
... A natural way to proceed is via data augmentation, a methodology introduced by Tan- ner and Wong (1987). The idea is based on the fact that the likelihood can always be well approximated given the entire ...
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Sequential importance sampling for bipartite graphs with applications to likelihood-based inference
... which constructs a surrogate for the likelihood based on the product of the full condi- tional distribution for each edge. Generally, this can be computed exactly. However, Robins et al. (2006) argue that ...
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Likelihood-based inference in some partially non-regular exponential families
... Figure 5.5 xl Plots, each showing the likelihood ratio test Statistic from 500 simulations where the data producing the statistic is randomly generated from a censored standard nor[r] ...
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Correcting for misclassification error in gross flows using double sampling: moment based inference vs likelihood based inference
... waves. This process, however, is affected by non-sampling errors such as response errors that cause misclassification error (Hogue and Flaim 1986; Kristiansson 1999). The existence of misclassification error in data used ...
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Empirical likelihood based inference in Poisson autoregressive model with conditional moment restrictions
... It is shown that our approach proposed, compared to the maximum likelihood estimator, the least squares estimator, and the weighted least squares estimator, yields more efficient estimators. Some simulation studies ...
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Targeted Maximum Likelihood Based Causal Inference
... In Section 3 we develop and present a general targeted MLE for any time-series data structure, applicable to sequentially randomized controlled trials with censoring and missingness, as well as longitudinal observational ...
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Topics in Likelihood Inference
... • standard likelihood asymptotics apply for inference based on the pro"le log-likelihood.. • in other examples, we see that pro"ling out large numbers of nuisance parameters can [r] ...
Indirect likelihood inference
... indirect inference (or CU-II), RMSE for the SBIL estimator is almost uniformly lower or equal to that of the II estimator, when the same auxiliary statistic is used (DPD, MA, nonlinear panel data, structural ...
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Neural Likelihood-free Inference
... Baydin, A. G., Heinrich, L., Bhimji, W., Gram-Hansen, B., Louppe, G., Shao, L., ... & Wood, F. (2018). Ef cient Probabilistic Inference in the Quest for Physics Beyond the Standard Model. arXiv preprint ...
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