Parametric Estimation
Maximal uniform convergence rates in parametric estimation problems
... of parametric estimators can be deduced from the absolute variation metric, which in turn can be bounded by functions of the Hellinger ...of parametric estimation ...
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Maximal uniform convergence rates in parametric estimation problems
... in estimation problems is to obtain estimators that converge to the target of estimation as fast as possible, in this sense making maximal or rate efficient use of the ...regular parametric, √ n ...
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Parametric Estimation of Various Protocols for Routing In Manets
... the parametric estimation of various protocols that are used for routing such as AODV (Ad-hoc On-demand Distance Vector), DSR (Dynamic Source Routing), and DSDV (Destination Sequence Distance Vector) ...
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Outliers in semi-parametric Estimation of Treatment Effects
... the estimation of the metric and are, as a consequence, practically impossible to be identified unless a for- mal outlier identification method is ...semi-parametric estimation of treatment ...
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Bayesian Approaches to Non-parametric Estimation of Densities on the Unit Interval
... Non-parametric estimation of probability density function of recovery rates of defaulted loans and bonds, which have support [0, 1], has been given considerable attention in the recent ...
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From Nonparametric Density Estimation to Parametric Estimation of Multidimensional Diffusion Processes
... of parametric estimation is that the minimum Hellinger distance estimation me- thod gives efficient and robust estimators ...parameter estimation for independent observations [7], for ...
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Non parametric estimation of an illness death stochastic model with recovery state
... the value of the process at time t the state occupied at that time. The states are either transient or absorbing. An absorbing state is a state from which further transitions cannot occur while a transient state allows ...
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Recent advances on the semi-parametric estimation of the long-range dependence coefficient
... proposed FAR estimator may be viewed also as providing a parametric approach to semi-parametric estimation. Bhansali and Kokoszka (1997) did not obtain ex- pressions for the asymptotic bias and ...
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Parametric estimation of discretely observed diffusions using the EM algorithm
... ML estimation for continuous observed v is straightfor- ward since a likelihood can be easily derived (see ...parameter estimation using the Expectation-Maximization ...
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Semi parametric estimation of joint large movements of risky assets
... When the set C corresponds to very large joint movements there are typically very few, if any, observations in this set. This scarcity of observations in C translates to the same lack of observations in its transformed ...
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A non Parametric Estimation of Service Level in a Discrete Context
... the estimation of the parameters of a demand distribution may be obtained using maximum likelihood estimators which estimate the parameters in order to make the observed data the most ...the estimation of ...
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A New Parametric Estimation Method for Graph based Clustering
... Relational clustering has received much attention from researchers in the last decade. In this paper we present a parametric method that employs a combination of both hard and soft clustering. Based on the ...
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Non-Parametric Estimation of ROC Curves in the Absence of a Gold Standard
... independence assumption the component distributions in a multivariate la- tent class model are not identifiable non-parametrically. Hence, in this paper we focus on a non-parametric maximum likelihood (ML) method ...
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Parametric Estimation Of Technical And Scale Efficiencies In Italian Citrus Farming
... As reported above, farm size might positively affect scale efficiency. It is the factor that contributes the most to conditioning scale efficiency (magnitude is equal to 0.040). This suggests that large-sized farms tend ...
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Non parametric Estimation of high-frequency Volatility and Correlation Dynamics
... We will now analyze the impact of the market microstructure noise on the optimum val- ues for the three parameters. To this purpose, we first set the standard deviation of the independent noise to ξ = 0.00005 (Low ...
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Bias in parametric estimation : reduction and useful side effects
... To check whether this is indeed the case a small simulation study has been designed where 50000 samples are simulated from the maximum likelihood fit shown in Table 1. Maximum likelihood is used to fit model 1 on each ...
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Non parametric estimation of the individual's utility map
... The usual experimental practice is to investigate choices in the regions of the triangle where models most differ from each other (e.g., Wu & Gonzalez, 1998). When the mod- els are tested in this way, the “best” ...
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A generic algorithm for reducing bias in parametric estimation
... In general, bias reduction will typically make most sense when applied to esti- mators whose distribution is approximately symmetric, since it will then most often improve the accuracy of inferences made when using ...
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Parametric Estimation in a Recurrent Competing Risks Model
... The main focus of this paper is the estimation of the parameter vector θ associated with the Q marginal distribution functions. To achieve more generality, we employ counting processes and martingales in the ...
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Parametric and non-parametric estimation of extreme earthquake event: the joint tail inference for mainshocks and aftershocks
... gins, which is a typical choice for modeling earthquake magnitude justified by the Gutenberg–Richter law (Gutenberg and Richter 1944). A natural alternative is to apply univariate extreme value theory to estimate these ...
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