[PDF] Top 20 Semiparametric Likelihood Ratio Inference
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Semiparametric Likelihood Ratio Inference
... case likelihood ratio confidence regions are gen- erally preferred over Wald-type confidence regions, except perhaps from a computational ...the likelihood ratio test as in Thomas and ... See full document
39
Inference of Population History Using a Likelihood Approach
... a likelihood ratio statistic that is based There are different approaches to analyzing this sort on two summary statistics, the mean pairwise sequence of data: Networks are a suggestive way to visualize the ... See full document
8
Inference for the Normal Mean with Known Coefficient of Variation
... log likelihood ratio statistic is, in general, the second most efficient estimator among the estimators discussed in this paper, and the cor- responding interval estimate has the best coverage prop- ... See full document
7
Semiparametric inference based on a class of zero-altered distributions
... In this article, we propose a new class of zero-altered distributions that can account for both under and over dispersion. This is similar in spirit to the work of Castillo and Perez-Casany (2005) who have recently ... See full document
21
Cancer Absolute Risk Projection with Incomplete Predictor Variables
... novel semiparametric maximum likelihood approach that accommodates incomplete predictor data under rare disease approximation for the estimation of odds ratio parameters and the distribution of ... See full document
141
Likelihood inference for small variance components
... for likelihood ratios as ...the likelihood ratio is a mixture of χ 2 ...and likelihood ratios which apply when parameters are near the boundary and which therefore interpolate between the ... See full document
16
Geostatistical inference in the presence of geomasking:A composite likelihood approach
... the ratio r = δ/φ, where δ is the standard deviation of the positional error process in the case of Gaussian geomasking or the maximum displacement distance under uniform geomasking, φ is the scale of the spatial ... See full document
22
Local Empirical Likelihood Diagnosis of Varying Coefficient Density Ratio Models Based on Case Control Data
... and inference for the two-sample varying-coefficient density-ratio model (1) by constructing the local empirical likelihood ...of inference has sampling properties similar to the ... See full document
7
Semiparametric approaches to inference in joint models for longitudinal and time-to-event data
... and Wald statistics for several models are shown in Table 3.1 (Models 2.1–2.8). The table excludes models that resulted in unstable estimation, gauged by a very large ratio of largest to smallest eigenvalues of ... See full document
97
Approximate maximum likelihood estimation for population genetic inference
... Abstract: In many population genetic problems, parameter estimation is obstructed by an intractable likelihood function. Therefore, approximate estimation methods have been developed, and with grow- ing ... See full document
22
Semiparametric Bayesian inference for time-varying parameter regression models with stochastic volatility
... A vast literature has demonstrated the gains from allowing for time-varying parame- ters in stochastic volatility models (TVP-SV models), when analyzing (macro)financial data (Primiceri, 2005; Cogley and Sargent, 2005; ... See full document
30
A penalized likelihood estimation approach to semiparametric sample selection binary response modeling
... Abstract: Sample selection models are employed when an outcome of in- terest is observed for a restricted non-randomly selected sample of the pop- ulation. We consider the case in which the response is binary and ... See full document
25
A penalized likelihood estimation approach to semiparametric sample selection binary response modeling
... Abstract: Sample selection models are employed when an outcome of in- terest is observed for a restricted non-randomly selected sample of the pop- ulation. We consider the case in which the response is binary and ... See full document
25
Semiparametric Bayesian inference in smooth coefficient models
... penalized likelihood approach, Silverman (1985) or Wahba (1983) for a discussion of splines and Smith and Kohn (1996) for a clever implementation of the spline approach using Bayesian model ... See full document
33
A semiparametric cluster detection method — a comprehensive power comparison with Kulldorff's method
... The semiparametric density ratio model essentially tests the homogeneity or equidistribution of two or more samples, therefore, besides Kulldorff ’ s circular scan window, the semiparametric method ... See full document
16
Composite likelihood inference for hidden Markov models for dynamic networks
... log-odds ratio that measures the conditional association between reciprocal relations given the latent ...composite likelihood inferential approach developed in this paper can be used in these extended ... See full document
26
Two-step semiparametric empirical likelihood inference
... semiparametric model in each bootstrap iteration, and thus are computa- tionally very expensive. The second proposal consists in adjusting the EL by a scale factor such that the adjusted (or rescaled) EL ... See full document
35
Semiparametric Estimation and Inference for Censored Regression Models.
... cient estimation via nonparametric maximum profile-likelihood estimation and compared their approach with the Buckley-James and rank estimators. Based on the missing-information prin- ciple such as “inverse ... See full document
86
Inference in the Presence of Likelihood Monotonicity for Polytomous and Logistic Regression
... for inference on the interaction effect on Hepatitis C, and four for inference on the interaction effect on non-ABC ...penalized likelihood asymptotic confidence intervals (17), and exact intervals ... See full document
11
Likelihood Inference for Generalized Integer Autoregressive Time Series Models
... Table 1 reports on some representative simulation results for I1 and I2 thinning to show the accuracy of the numerical methods and how the computing time increases when there are increases in the (i) number of ... See full document
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