[PDF] Top 20 Likelihood-Free Inference in High-Dimensional Models
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Likelihood-Free Inference in High-Dimensional Models
... Such models may include hyperparameters like genome-wide mutation and recombina- tion rates or parameters regarding the demographic history, along with locus-specific parameters that allow for between- locus ... See full document
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Bayesian Inference for High Dimensional Models: Convergence Properties and Computational Issues.
... the likelihood is mathemati- cally intractable but we find an approximation by expansion around the posterior mode, which is the group lasso solution in generalized linear model setting for the choice of ...the ... See full document
136
The Factor Lasso and K Step Bootstrap Approach for Inference in High Dimensional Economic Applications
... A more interesting comparison can be made by looking more closely and considering the variable and factor selection results. The “Post Double Selection” procedure ends up selecting three variables for estimating the ... See full document
82
Variational Bayes inference in high dimensional time varying parameter models
... We show, via a Monte Carlo exercise and an empirical application, that our proposed algorithm works well in high-dimensional sparse time-varying parameter settings. In the Monte Carlo exercise we compare ... See full document
61
Particle filter-based approximate maximum likelihood inference asymptotics in state-space models
... filter goes from selecting one particle to another one. In addition, the stochastic properties of the approximation are more difficult to analyse, because of the dependence across θ. Pitt [5] proposed a smoothed version of ... See full document
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Quasi Maximum Likelihood Analysis of High Dimensional Constrained Factor Models
... factor models, a large k leads to a larger number of parameters being ...factor models against standard factor models becomes weak, which makes constrained factor less attractive in ...factor ... See full document
93
Methods of likelihood based inference for constructing stochastic climate models
... The results, shown in Figure 5.8, are for the case of updating all components simultaneously. Notice that the estimates smoothly converge for increasing m which implies that sufficient samples have been used for each ... See full document
235
Composite likelihood inference for hidden Markov models for dynamic networks
... combination of latent states keeping in mind the constraints defined in equation (2) and (3). Based on these results, we are able to identify three groups having quite a different profile. The first hidden state ... See full document
26
Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models
... maximum likelihood estimate obtained by fitting the model to the correlation matrix is shown in the lower-diagonal part of Table 1; note that this estimate is not a correlation matrix (not all the italicized ... See full document
22
Efficient Learning and Inference for High-dimensional Lagrangian Systems
... Figure 11.1a illustrates a local minimum that occurs in a three-dimensional problem if two-dimensional search submanifolds are employed. The figure illustrates a cage-like obstacle created by carving cubes ... See full document
143
Semiparametric Likelihood Ratio Inference
... a likelihood ratio statistic requires the definition of a like- lihood ...parametric models this is the density of the ob- servations, while empirical likelihood theory uses the product Q P X i ... See full document
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Generalized quasi maximum likelihood inference for periodic conditionally heteroskedastic models
... volatility models have continued to capture the interest of researchers in the statistical and …nancial econometric literature ...volatility models have proved useful are …nancial stock return series, which ... See full document
45
Simulated likelihood inference for stochastic volatility models using continuous particle filtering
... Estimates of the jump probabilities (times) and average jumps size allow us to better understand the contribution of these components to volatility, especially during periods of market stress. Un- derstanding this ... See full document
27
Maximum likelihood estimation and inference for high dimensional nonlinear factor models with application to factor augmented regressions
... the likelihood function is nonconcave with respect to the ...large dimensional nonlinear panels to the current setup, because they either require there is only individual e¤ects or time e¤ects (see for ... See full document
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Statistical Inference For High-Dimensional Linear Models
... many high dimensional covariates, Zhang & Zhang (2014); Javanmard & Montanari (2014a); van de Geer et ...with high dimensional covariates (or IVs), Gautier & Tsybakov (2011); ... See full document
253
Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models
... Our paper opens up a wide range of extensions and opportunities for future research. One possibility is to use the tools provided by Bayesian optimization to tackle the chal- lenging problem of ... See full document
47
High Dimensional Generalized Empirical Likelihood for Moment Restrictions with Dependent Data
... for high dimensional moment restriction models with increasing number of parameters and weakly dependent ...the high dimensional sparse parameter situation with p > r, although the ... See full document
45
Scalable Collapsed Inference for High Dimensional Topic Models
... and trained the model on the rest of the corpus. Then, we split each test document in half, esti- mated local parameters on first half and finally computed the log-likelihood of the remaining half of the document. ... See full document
10
High-dimensional Statistical Inference: from Vector to Matrix
... statistical inference has been a very active area in the recent ...in high-dimensional statistical inference, including sparse signal recovery (compressed sensing) and low- rank matrix ... See full document
247
High dimensional painlev´e integrable schwarzian boussinesq models
... In summary, we have extended the (1+1)-dimensional Schwartzian Boussinesq equation to the arbitrary dimensional system with selecting the high dimensional Schwarzian derivatives. We have shown ... See full document
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