scholarly journals ALTERNATIVE ASYMPTOTICS AND THE PARTIALLY LINEAR MODEL WITH MANY REGRESSORS

2016 ◽  
Vol 34 (2) ◽  
pp. 277-301 ◽  
Author(s):  
Matias D. Cattaneo ◽  
Michael Jansson ◽  
Whitney K. Newey
Keyword(s):  
Degrees Of Freedom ◽  
Asymptotic Theory ◽  
Empirical Studies ◽  
Structural Effect ◽  
Partially Linear Model ◽  
Partially Linear ◽  
Many Instruments ◽  
Small Bandwidth ◽  
Distributional Approximations ◽  
Inference Methods

Many empirical studies estimate the structural effect of some variable on an outcome of interest while allowing for many covariates. We present inference methods that account for many covariates. The methods are based on asymptotics where the number of covariates grows as fast as the sample size. We find a limiting normal distribution with variance that is larger than the standard one. We also find that with homoskedasticity this larger variance can be accounted for by using degrees-of-freedom-adjusted standard errors. We link this asymptotic theory to previous results for many instruments and for small bandwidth(s) distributional approximations.

scholarly journals INFERENCE IN NONPARAMETRIC SERIES ESTIMATION WITH SPECIFICATION SEARCHES FOR THE NUMBER OF SERIES TERMS

2020 ◽  
pp. 1-35
Author(s):  
Byunghoon Kang
Keyword(s):  
Confidence Intervals ◽  
Confidence Bands ◽  
Tuning Parameter ◽  
Partially Linear Model ◽  
Finite Sample ◽  
Wage Elasticity ◽  
Conditional Mean ◽  
Partially Linear ◽  
Model Setup ◽  
Inference Methods

Nonparametric series regression often involves specification search over the tuning parameter, that is, evaluating estimates and confidence intervals with a different number of series terms. This paper develops pointwise and uniform inferences for conditional mean functions in nonparametric series estimations that are uniform in the number of series terms. As a result, this paper constructs confidence intervals and confidence bands with possibly data-dependent series terms that have valid asymptotic coverage probabilities. This paper also considers a partially linear model setup and develops inference methods for the parametric part uniform in the number of series terms. The finite sample performance of the proposed methods is investigated in various simulation setups as well as in an illustrative example, that is, the nonparametric estimation of the wage elasticity of the expected labor supply from Blomquist and Newey (2002, Econometrica 70, 2455–2480).

ON THE USE OF PARTIALLY LINEAR MODEL IN IDENTIFICATION OF ARCING-FAULT LOCATION ON OVERHEAD HIGH-VOLTAGE TRANSMISSION LINES

2004 ◽  
Vol 14 (06) ◽  
pp. 1975-1985
Author(s):  
RASTKO ŽIVANOVIĆ
Keyword(s):  
Linear Model ◽  
Dynamic Behavior ◽  
Nonlinear System Identification ◽  
Identification Problem ◽  
Parametric Modeling ◽  
Partially Linear Model ◽  
Practical Reasons ◽  
Partially Linear ◽  
Pull Out ◽  
Arcing Fault

The task of locating an arcing-fault on overhead line using sampled measurements obtained at a single line terminal could be classified as a practical nonlinear system identification problem. The practical reasons impose the requirement that the solution should be with maximum possible precision. Dynamic behavior of an arc in open air is influenced by the environmental conditions that are changing randomly, and therefore the useful practically application of parametric modeling is out of question. The requirement to identify only one parameter is yet another specific of this problem. The parameter we need is the one that linearly correlates the voltage samples with the current derivative samples (inductance). The correlation between the voltage samples and the current samples depends on the unpredictable arc dynamic behavior. Therefore this correlation is reconstructed using nonparametric regression. A partially linear model combines both, parametric and nonparametric parts in one model. The fit of this model is noniterative, and provides an efficient way to identify (pull out) a single linear correlation from the nonlinear time series.

Approximate Bayesian inference for joint linear and partially linear modeling of longitudinal zero-inflated count and time to event data

2021 ◽  
pp. 096228022110028
Author(s):  
T Baghfalaki ◽  
M Ganjali
Keyword(s):  
Linear Model ◽  
Joint Modeling ◽  
Partially Linear Model ◽  
Event Data ◽  
Time To Event ◽  
Data Set ◽  
Partially Linear ◽  
Time To Event Data ◽  
Count Response ◽  
Approximate Bayesian

Joint modeling of zero-inflated count and time-to-event data is usually performed by applying the shared random effect model. This kind of joint modeling can be considered as a latent Gaussian model. In this paper, the approach of integrated nested Laplace approximation (INLA) is used to perform approximate Bayesian approach for the joint modeling. We propose a zero-inflated hurdle model under Poisson or negative binomial distributional assumption as sub-model for count data. Also, a Weibull model is used as survival time sub-model. In addition to the usual joint linear model, a joint partially linear model is also considered to take into account the non-linear effect of time on the longitudinal count response. The performance of the method is investigated using some simulation studies and its achievement is compared with the usual approach via the Bayesian paradigm of Monte Carlo Markov Chain (MCMC). Also, we apply the proposed method to analyze two real data sets. The first one is the data about a longitudinal study of pregnancy and the second one is a data set obtained of a HIV study.

scholarly journals New Restricted Liu Estimator in a Partially Linear Model

2020 ◽  
Vol 2020 ◽  
pp. 1-6
Author(s):  
Jibo Wu ◽  
Yong Li
Keyword(s):  
Asymptotic Normality ◽  
Linear Model ◽  
Simulation Study ◽  
Linear Constraints ◽  
Partially Linear Model ◽  
Liu Estimator ◽  
Numerical Example ◽  
Partially Linear

In this paper, we introduce a new restricted Liu estimator in a partially linear model when addition linear constraints are assumed to hold. We also consider the asymptotic normality of the new estimator. Finally, a numerical example and a simulation study are listed to illustrate the performance of the new estimator.

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