[PDF] Top 20 Semiparametric quasi-likelihood estimation with missing data
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Semiparametric quasi-likelihood estimation with missing data
... Quasi-likelihood estimation is routinely used in econometrics and statistics to estimate known index structure models for binary, counts and fractional responses, see for example McCullagh & ... See full document
26
Semiparametric estimation of a panel data proportional hazards model with fixed effects
... This paper has presented nonparametric estimators of the baseline and integrated baseline hazard functions in a panel data proportional hazards model with fixed effects. The paper has also shown how the parametric ... See full document
51
Semiparametric Likelihood Ratio Inference
... a likelihood ratio statistic requires the definition of a like- lihood ...empirical likelihood theory uses the product Q P X i ...dimensional likelihood function in this ...a likelihood is ... See full document
39
A penalized likelihood estimation approach to semiparametric sample selection binary response modeling
... present data on Americans’ social backgrounds, enduring political predispositions, social and political values, perceptions and evaluations of groups and candidates, opinions on questions of public policy, and ... See full document
25
A penalized likelihood estimation approach to semiparametric sample selection binary response modeling
... Statistical methods correcting for non-random selection have been devel- oped. Many of these concern models where the response variable is Gaussian [5, 16, 11, 22, 25, 40]. There are also a number of works that go beyond ... See full document
25
Estimation and tests for power-transformed and threshold GARCH models
... maximum likelihood estimators for ARCH and GARCH model ...moment), quasi-maximum likelihood estimators (QMLE) are not asymptotically normal and suffer from slow convergence rate and complex ... See full document
39
The Uncertainty Reduction for the Refined Sample Mean of Combined Quantities
... available data is not large ...maximum likelihood estimation (QMLE) method for mean value estimation of a quasi-normal distribution is ...as quasi-symmetric quantiles and fuses ... See full document
6
An Expectation-Maximization–Likelihood-Ratio Test for Handling Missing Data
... parameter estimation, because the sample size for the are highly useful for mapping traits that may ap- incomplete data is less than it would be if the data were ply to human diseases (Knoblauch and ... See full document
12
Smoothed Empirical Likelihood Inference for ROC Curves with Missing Data
... empirical likelihood ratio statistic, derive its limiting- distribution and construct the empirical likelihood confi- dence interval for the ROC ...empirical likelihood interval ... See full document
7
Semiparametric Estimators for the Regression Coefficients in the Linear Transformation Competing Risks Models with Missing Cause of Failure
... the missing cause is the cause of interest, we use multiple imputation procedures (Rubin, 1987, 1996) to impute the missing causes that in turn give us com- pleted data ...completed data sets, ... See full document
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Statistical Methods for Non-Ignorable Missing Data With Applications to Quality-of-Life Data.
... marginal likelihood methods (Cox and Reid, 2004; Varin et ...conventional likelihood-based method. The pseudo-likelihood methods can be viewed as an extension of composite marginal likelihood ... See full document
124
Semiparametric Efficient Estimation of Treatment Effect in a Pretest-Posttest Study with Missing Data
... collected. Missing posttest response for some subjects is routine, and disregarding these missing cases can lead to biased and inefficient ...no data are missing, let alone on an accepted ... See full document
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Estimation in semiparametric models with missing data
... (with missing responses and/or covariates), but also to any other semiparametric model with missing ...study semiparametric efficiency bounds and efficient estimation of parameters ... See full document
25
Shrinkage Estimation of Semiparametric Model with Missing Responses for Cluster Data
... Abstract This paper simultaneously investigates variable selection and imputation estimation of semiparametric partially linear varying-coefficient model in that case where there exist m[r] ... See full document
9
Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data
... of estimation and inference in the presence of model misspecification are important for several ...the Quasi-Maximum Likelihood Estimates (QMLE) converge to the true parameter value despite the ... See full document
27
Two-step semiparametric empirical likelihood inference
... the modified test there is no need for undersmoothing, which means that, in contrast to alternative methods, the proposed inference method is asymptot- ically valid with a cross-validated bandwidth for the first-step. ... See full document
35
Estimating from Cross sectional Categorical Data Subject to Misclassification and Double Sampling: Moment based, Maximum Likelihood and Quasi Likelihood Approaches
... categorical data in the presence of misclassification and double ...a missing data problem using the misclassification ...maximum likelihood estimation via the EM ...a missing ... See full document
34
Carrots versus sticks: Rewarding commuters for avoiding the rush hour a study of willingness to participate
... purpose was to collect a large sample of revealed preference (RP) data regarding the impact of rewards on daily commuting behavior during the morning rush-hour. During a period of 13 consecutive weeks in Autumn, ... See full document
23
Modelling asymmetric conditional heteroskedasticity in financial asset returns: an extension of Nelson’s EGARCH model
... financial data (see Holly, 2010; Chung, 2012; Drost & Klassenn, 1996; Engle & Gonzale- Rivera, 1991). The first is volatility clustering. This is where large changes tend to be followed by large changes ... See full document
37
A semi parametric GARCH (1, 1) estimator under serially dependent innovations
... conditional variance expressed as a function of lagged conditional variance and lagged squared innovations. Deterministic models come under parametric, semi-parametric or non-parametric sub- approaches depending on the ... See full document
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