semi-nonparametric
Nonparametric and Semi Nonparametric Recreational Demand Analysis
... The semi-nonparametric (SNP) models presented here provide both a means to deal with multiple explanatory variables more easily than with NP methods, and unlike NP, embed the parametric model, thereby ...
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Semi-nonparametric estimation of regression-based survival models
... This paper proposes new semi-nonparametric survival models that generalize both an explanatory variable and unobserved heterogeneity. The former is Box-Cox transformation and the latter is a Hermite series. ...
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Penalized Sieve Estimation and Inference of Semi-Nonparametric Dynamic Models: A Selective Review
... economic semi-nonparametric time series ...for semi-nonparametric dynamic models with nonlinear, non-Gaussian latent ...complicated semi-nonparametric dynamic ...to ...
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Implicit probability distribution for WTI options: The Black Scholes vs. the semi-nonparametric approach
... This paper contributes to the literature on the estimation of the Risk Neutral Density (RND) function by modeling the prices of options for West Texas Intermediate (WTI) crude oil that were traded in the period between ...
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Semi-nonparametric modeling of topological domain formation from epigenetic data
... convex semi-nonparametric approach called nTDP based on Bernstein polynomials to explore the joint effects of histone markers on TAD formation as well as predict TADs solely from the histone ...
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Sieve Inference on Semi-nonparametric Time Series Models
... For semi-nonparametric time series models, we could compute various “pre-asymptotic” Wald sta- tistics using various existing LRV estimators for regular functionals of (misspeci…ed) parametric time series ...
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Measuring firm size distribution with semi-nonparametric densities
... (log) semi-nonparametric (log- SNP) distribution that nests the lognormal and enables better fits in the upper tail of the distribution through the introduction of new ...
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Advances in Semi-Nonparametric Density Estimation and Shrinkage Regression
... A plethora of results pertaining to density approximation or density estimation are avail- able in the literature. In the former case, the exact density function is approximated by means of various statistical or ...
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Semi-Nonparametric Estimation and Misspecification Testing of Diffusion Models
... We propose novel misspeci…cation tests of semiparametric and fully parametric univariate di¤usion models based on the estimators developed in Kristensen (Journal of Econometrics, 2010). We …rst demonstrate that given a ...
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Admissible clustering of aggregator components: a necessary and sufficient stochastic semi nonparametric test for weak separability
... Three approaches have tried to correct the initial nonparametric approach. Fleissig and Whitney (2003) have suggested a new algorithm to solve the Afriat inequalities. They have moreover showed that their test ...
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Geostatistical radar raingauge combination with nonparametric correlograms: methodological considerations and application in Switzerland
... Recently, nonparametric correlograms based on spatially complete radar rainfall fields have been used in combining radar and raingauge data (Cassiraga et ...of nonparametric correl- ograms is fast and ...
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Real Time Data Revisions and the PCE Measure of Inflation
... Robinson (1998) noted that the nonparametric methodology takes into account heteroskedasticity but not autocorrelation. Even though the parameters are not affected, autocorrelation needs to be addressed since it ...
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Non-parametric methods for circular-circular and circular-linear
... combine nonparametric estimates of the marginal densities of the circular and linear components with the use of class of nonparametric copulas, known as empirical Bernstein copulas, to model the dependence ...
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Application Of The Empirical Likelihood Method In Proportional Hazards Model
... a nonparametric likelihood ratio idea to construct confidence intervals for the survival ...provides nonparametric maximum likelihood estimation which has a long history in sur- vival ...
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... use a two tier approach with fi xed numbers of units al- lowed for testing in each tier. If results are suffi ciently positive in the fi rst stage (tier) of testing, then the study stops. If insuffi ciently positive, the study ...
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Nonparametric regression method for estimating the error variance in unistage sampling
... following section gives a review of nonparametric regression, states a nonparametric regression based estimator of the total, gives expressions for its bias and variance, and suggests ho[r] ...
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Nonparametric Stochastic Volatility
... both nonparametric and parametric estimates of stochastic volatility ...the nonparametric case but allow for market microstructure noise (when handling the …rst-stage spot volatility estimates) as well as ...
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Nonparametric Quantile Estimation
... a nonparametric version of a quantile estimator, which can be obtained by solving a simple quadratic programming problem and provide uniform convergence statements and bounds on the quantile property of our ...
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Nonparametric Sparsity and Regularization
... In this work we are interested in the problems of supervised learning and variable selection when the input-output dependence is described by a nonlinear function depending on a few variables. Our goal is to consider a ...
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Bayesian Nonparametric Crowdsourcing
... We have proposed two new Bayesian nonparametric models to merge the information provided by the users in a crowdsourcing system. In addition, the algorithms detect clusters of users that have similar behaviors and ...
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