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parametric inference

PROBABILISTIC PROPERTIES AND PARAMETRIC INFERENCE OF SMALL VARIANCE NONLINEAR SELF-STABILIZING STOCHASTIC DIFFERENTIAL EQUATIONS

PROBABILISTIC PROPERTIES AND PARAMETRIC INFERENCE OF SMALL VARIANCE NONLINEAR SELF-STABILIZING STOCHASTIC DIFFERENTIAL EQUATIONS

... Remark 3.3. Consider Example (7). The process (X t ) is equal to its Gaussian approxi- mation, i.e. the remainder term R ε (t) of Theorem 3.1 is null. Moreover, D(θ, t, ε, x) ≡ 0. 4. Parametric inference ...

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Parametric inference for hypoelliptic ergodic diffusions with full observations

Parametric inference for hypoelliptic ergodic diffusions with full observations

... to the Lebesgue measure still exist. That is the case when the noise is propagated to all the coordinates through the drift term. Hypoelliptic SDEs present a number of extra challenges in comparison to elliptic systems. ...

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Parametric inference and forecasting in continuously invertible volatility models

Parametric inference and forecasting in continuously invertible volatility models

... Statistical inference under continuous invertibility Consider ˆθ n = argmin θ∈ Θ S ˆ n ( θ ) the M-estimator associated with the QLIK cri- teria ( 6 ) where ( ˆg t ) is obtained from the approximative SRE ( 10 ...

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Non-Parametric Inference of Transition Probabilities Based on Aalen-Johansen Integral Estimators for Acyclic Multi-State Models: Application to LTC Insurance

Non-Parametric Inference of Transition Probabilities Based on Aalen-Johansen Integral Estimators for Acyclic Multi-State Models: Application to LTC Insurance

... Our approach is based on recent alternatives to the canonical Aalen-Johansen estimator for transition probabilities, which is adapted to Markov multi-state models, which have been developed for some particular non-Markov ...

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Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation

Virtual age models with time-dependent covariates: A framework for simulation, parametric inference and quality of estimation

... Very few articles work with models that include both covariates and imperfect main- tenances. Wu and Scarf [45] consider the ARA models with dynamic covariates, but no estimation procedure is given. Pe˜ na [46] derives ...

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PARAMETRIC INFERENCE FOR SMALL VARIANCE AND LONG TIME HORIZON MCKEAN-VLASOV DIFFUSION MODELS

PARAMETRIC INFERENCE FOR SMALL VARIANCE AND LONG TIME HORIZON MCKEAN-VLASOV DIFFUSION MODELS

... AMS Classification. 60J60, 60J99, 62F12, 62M05 1. Introduction We develop an approximate likelihood approach for estimating the unknown parameters of a dynamical model subject to three sources of forcing: the geometry of ...

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Parametric inference for mixed models defined by stochastic differential equations

Parametric inference for mixed models defined by stochastic differential equations

... statistical parametric approach commonly used to analyze this longitudinal data is through mixed models: the same regression function is used for all the subjects, but the regression parameters differ between the ...

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Bayesian non parametric inference of discrete valued networks

Bayesian non parametric inference of discrete valued networks

... The Stochastic Block Model (SBM) [2, 9] is a more flexible model. Given a network, it assumes that each vertex belongs to a latent class among K classes and uses a K × K connectivity matrix to describe the connection ...

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Mod/Resc Parsimony Inference

Mod/Resc Parsimony Inference

... the problem cannot be approximated within a factor of (n + m) 1/3−ε unless P = NP. 4 Fixed-parameter tractability In this section, we explore a parameterized complexity approach [4, 9, 14] for the Mod/Resc Par- simony ...

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Survey sampling targeted inference

Survey sampling targeted inference

... Throughout the manuscript, we denote µf ≡ R f dµ and kf k 2,µ ≡ (µf 2 ) 1/2 for any measure µ and function f (measurable and integrable with respect to µ). 2.1 Retrieving the observations by survey sampling As explained ...

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Topological Inference via Meshing

Topological Inference via Meshing

... From a practical point of view, the persistence diagram of a simplicial filtration (i.e. a finite family of nested finite abstract simplicial complexes) can be computed using the persist[r] ...

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Geometric and Topological Inference

Geometric and Topological Inference

... For example, when one claims that an abstract simplicial complex K is homeomorphic or homotopy equiv- alent to a topological space X, it is meant that the underlying space of any geometr[r] ...

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Randomized parcellation based inference.

Randomized parcellation based inference.

... 2.2. Parcellation and Ward algorithm In functional neuroimaging, brain atlases are often used to provide a low-dimensional representation of the data by considering signal averages within groups of voxels (re- gions of ...

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Approximate Decentralized Bayesian Inference

Approximate Decentralized Bayesian Inference

... distributed inference (Paskin and Guestrin, 2004) requires the formation of a spanning tree of nodes in the network and message passing; asynchronous distributed learning of topic models (Asuncion et ...

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Piece-Rates, Principal-Agent, and Productivity Profiles: Parametric and Semi-Parametric Evidence

Piece-Rates, Principal-Agent, and Productivity Profiles: Parametric and Semi-Parametric Evidence

... The model developed in this paper incorporates the important as- pects of the production process at the Britannia mine: asymmetric in- formation, team production, and heterogeneous workers. Output is a function of worker ...

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Bayesian inference algorithm on Raw

Bayesian inference algorithm on Raw

... For the medium grain implementation, the problem size is split among the tiles (not replicated), so the total load-up time should remain about the same as the number of [r] ...

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Solving Parametric Polynomial Systems

Solving Parametric Polynomial Systems

... At this step, one have a partition of Π constituted by W D and a collection of cells of dimension δ. Note that compared with a partial CAD adapted to E ∪ F, we have replaced the n − δ projection step by the computation ...

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Parametric schema inference for massive JSON datasets

Parametric schema inference for massive JSON datasets

... 1 , . . . , J n : c T ⇒ {| J J 1 K, . . . , J J n K |} ⊆ J T K Proof. We prove it by mutual induction on the size of the inference proof and by cases on the last applied rule. The base rules are trivial. The cases ...

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Practical aspects of gene regulatory inference via conditional inference forests from expression data

Practical aspects of gene regulatory inference via conditional inference forests from expression data

... conditional inference trees (CITs) and forest (CIFs) to infer gene regulatory networks from synthetic and real-life ...conditional inference framework suggested by [Hothorn, et ...GRN inference ...

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Revisiting Subnet Inference WISE-ly

Revisiting Subnet Inference WISE-ly

... We however note two things. First, WISE processes back- wards the addresses list. Indeed, many of our observations showed that contra-pivots are usually found among the first subnet addresses. Going backwards therefore ...

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