Semi-parametric
Semi parametric density estimation
... a parametric estimate of g( t ) ...a parametric family which is defined on a limited range is convenient here; our density estimate cannot logically take any value other than 0 for t < 0 when we are ...
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Semi parametric Bayesian Partially Identified Models based on Support Function
... identified parametric and semi-parametric models, under mild assumptions the poste- rior is asymptotically normal due to the Bernstein von Mises theorem and hence its shape is not affected anymore ...
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Bayesian inference for a semi parametric copula based Markov chain
... tions for constructing stationary first-order Markov models based on a copula. They show that the Chapman-Kolmogorov equations are satisfied for such models. Ibragimov (2009) ex- tend the conditions presented by Darswo ...
27
Semi parametric estimation of joint large movements of risky assets
... a semi-parametric method from extreme value ...is semi-parametric because no specific form for the dependence structure of the extreme movements is ...
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Asymptotic behavior of some Bayesian nonparametric and semi-parametric procedures
... the frequentist distribution of √ n( ˆ β − β) are approximately the same, where ˆ β is some suitable classical estimator of β and the cumulative hazard function is given a L´ evy process prior. This implies that the ...
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Economic conditions and birth spacing in Colombia: a semi parametric approach
... use parametric models (Retherford et al. 2010), semi-parametric analysis (Adsera and Menéndez, 2011) or non-parametric ...of parametric modeling is the need to assume a normal ...
28
Instrumental variable estimation in semi parametric additive hazards models
... The semi-parametric additive hazard model which can include time-independent and time-dependent covariate effects is particularly suited for the two-stage residual inclusion method, since it allows direct ...
17
Recent advances on the semi-parametric estimation of the long-range dependence coefficient
... up to a nite number of parameters. In the semi-parametric context, only the long- range dependence of the time-series is assumed and the exact formof L is not known. There are dierent techniques for ...
14
Determinants of bank efficiency: Evidence from a semi parametric methodology
... In this paper, we use a semi-parametric two-stage model to examine the effect of bank- specific, industry-specific and macroeconomic determinants of bank efficiency. This method, proposed by Simar and ...
27
Semi-parametric risk prediction models for recurrent cardiovascular events in the LIPID study
... novel semi-para- metric proportional hazards model and Lin [14] pre- sented general theory for this ...method. Semi-parametric models have further been updated and applied by Pepe and Cai [15] for ...
9
A semi parametric GARCH (1, 1) estimator under serially dependent innovations
... under parametric, semi-parametric or non-parametric sub- approaches depending on the assumptions about the structure of the ...extreme, parametric models make explicit both the ...
68
Semi parametric dynamic time series modelling with applications to detecting neural dynamics
... This work is motivated by the difficulties encountered in constructing time series models when the niether the factors driving the dynamics of their pa- rameters nor the relationship between the resolution of the data ...
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Dependence analysis between foreign exchange rates : a semi parametric copula approach
... any parametric family chosen requires testing for the appropriateness of the chosen marginals ...as semi-parametric copula estimation, where a parametric copula is ...
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A semi-parametric regression model for analysis of middle censored lifetime data
... The present study discussed the semi-parametric regression problem for the anal- ysis of middle censored data. A maximization procedure for finding the NPMLE is developed and its consistency established. The ...
14
Semi-Parametric Sampling for Stochastic Bandits with Many Arms
... a parametric reward model with arm features, contextual ban- dit algorithms are more efficient, but they can also suffer from large regret in practical applications, due to the reward esti- mation bias from ...
8
Semi and non semi parametric models for reliability analysis
... In this Paper, The purpose of applying the Cox-regression is mainly for comparison between the treatment regimens or comparison of dosage level of radiotherapy, their ultimate aim being identification of the prognostic ...
11
Non-Parametric and Semi-Parametric RSSI/Distance Modeling for Target Tracking in Wireless Sensor Networks
... non- parametric regression model, while the second one is a semi-parametric regression model that combines the well-known log-distance theoretical propagation model with a non-linear fluctuation ...
6
Modelling fertility in rural South Africa with combined nonlinear parametric and semi parametric methods
... The semi-parametric part of our model, using Gauss- ian process regression over other covariates, successfully manages to model the SES pattern of fertility without simply assuming the relationship to be ...
12
A comparison of block and semi-parametric bootstrap methods for variance estimation in spatial statistics
... Table 2: True values of σ12 and approximates of the NBias, NVar and NMSE for MBB and SPB estimators σ ˆ12 based on exponential covariogram.. Table 3: True values of σ12 and approximates [r] ...
35
Semi-Parametric Models for Independent Component Analysis.
... One key difference of our model to other mixture models is that we use a suitable sequence of completely known densities with only the weights and the number of components being estimate[r] ...
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