Results 101 to 110 of about 6,656,167 (244)
A remark on semiparametric models
Journal of Multivariate Analysis, 1986 Consider a semiparametric model (X,A,\({\mathcal P})\) with interesting Euclidean parameter \(\kappa\) : \({\mathcal P}\to R_ k\) to be estimated. Assume that \({\mathcal P}\) is a differentiable family of mutually absolutely continuous probability measures and the parameter \(\kappa\) is a functional on \({\mathcal P}\).
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Testing Distributional Granger Causality With Entropic Optimal Transport
Journal of Time Series Analysis, EarlyView.ABSTRACT We develop a novel nonparametric test for Granger causality in distribution based on entropic optimal transport. Unlike classical mean‐based approaches, the proposed method directly compares the full conditional distributions of a response variable with and without the history of a candidate predictor.
Tao Wang
wiley +1 more source
Semiparametric Estimation of Single-Index Transition Intensities [PDF]
This research develops semiparametric kernel-based estimators of state-specific conditional transition intensities, h(y|x), for duration models with right-censoring and/or multiple destinations (competing risks).
Tue Gorgens
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A Semiparametric Bayesian Approach to Heterogeneous Spatial Autoregressive Models
EntropyMany semiparametric spatial autoregressive (SSAR) models have been used to analyze spatial data in a variety of applications; however, it is a common phenomenon that heteroscedasticity often occurs in spatial data analysis.
Ting Liu, Dengke Xu, Shiqi Ke
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Semiparametric volatility model with varying frequencies
Communications in Statistics - Simulation and ComputationIn extracting time series data from various sources, it is inevitable to compile variables measured at varying frequencies as this is often dependent on the source. Modeling from these data can be facilitated by aggregating high frequency data to match the relatively lower frequencies of the rest of the variables.
Jetrei Benedick R. Benito +2 more
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Parametric Time‐Variation in the Unconditional Volatility: Estimation and Inference
Journal of Time Series Analysis, EarlyView.ABSTRACT We propose modeling time‐variation in the unconditional volatility by augmenting the standard GARCH model by a deterministic time‐varying intercept. The model, called the additive time‐varying (ATV‐)GARCH model, can be interpreted as a reduced form of a model including covariates and can be derived from a multiplicative decomposition of ...
Niklas Ahlgren +2 more
wiley +1 more source
Inference on semiparametric models with discrete regressors [PDF]
, 1993 We study statistical properties of coefficient estimates of the partially linear regression model when some or all regressors, in the unknown part of the model, are discrete. The method does not require smoothing in the discrete variables.
Delgado, Miguel A., Mora, Juan
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Models and methodologies for credit scoring in personal banking: A literature review
Revista CEA, 2017 This paper provides a literature review on risk scoring models for credit granting in personal banking. The methods by Abdou & Pointon (2011), Glennon, Kiefer, Larson, & Choi (2008), and Saavedra-García (2010) are considered.
David Esteban Rodríguez-Guevara +2 more
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Efficient Estimation of Copula-based Semiparametric Markov Models [PDF]
This paper considers efficient estimation of copula-based semiparametric strictly stationary Markov models. These models are characterized by nonparametric invariant (one-dimensional marginal) distributions and parametric bivariate copula functions ...
Yanping Yi, Xiaohong Chen, Wei Biao Wu
core
Nonparametric and semiparametric estimation with discrete regressors [PDF]
, 1995 This note is concerned with nonparanetric and semiparametric inference in regression models where regressors are not continuous. In economic practice, few explanatory variables are continuous.
Delgado, Miguel A., Mora, Juan
core