			Home About us Contact

Profile Likelihood (profile + likelihood)

Distribution by Scientific Domains

Mathematics and Statistics	90%

Selected Abstracts

Meta-Analysis of Binary Data Using Profile Likelihood by BÖHNING, D., KUHNERT, R., and RATTANASIRI, S.

BIOMETRICS, Issue 2 2009
Eloise Kaizar
No abstract is available for this article. [source]

Improved likelihood inference for the roughness parameter of the GA0 distribution

ENVIRONMETRICS, Issue 4 2008
Michel Ferreira da Silva
Abstract This paper presents adjusted profile likelihoods for ,, the roughness parameter of the distribution. This distribution has been widely used in the modeling, processing and analysis of data corrupted by speckle noise, e.g., synthetic aperture radar images. Specifically, we consider the following modified profile likelihoods: (i) the one proposed by Cox and Reid, and (ii) approximations to adjusted profile likelihood proposed by Barndorff,Nielsen, namely the approximations proposed by Severini and one based on results by Fraser, Reid and Wu. We focus on point estimation and on signalized likelihood ratio tests, the parameter of interest being the roughness parameter that indexes the distribution. As far as point estimation is concerned, the numerical evidence presented in the paper favors the Cox and Reid adjustment, and in what concerns signalized likelihood ratio tests, the results favor the approximation to Barndorff7mdash;Nielsen's adjustment based on the results by Fraser, Reid and Wu. An application to real synthetic aperture radar imagery is presented and discussed. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Models for the estimation of a ,no effect concentration'

ENVIRONMETRICS, Issue 1 2002
Ana M. Pires
Abstract The use of a no effect concentration (NEC), instead of the commonly used no observed effect concentration (NOEC), has been advocated recently. In this article models and methods for the estimation of an NEC are proposed and it is shown that the NEC overcomes many of the objections to the NOEC. The NEC is included as a threshold parameter in a non-linear model. Numerical methods are then used for point estimation and several techniques are proposed for interval estimation (based on bootstrap, profile likelihood and asymptotic normality). The adequacy of these methods is empirically confirmed by the results of a simulation study. The profile likelihood based interval has emerged as the best method. Finally the methodology is illustrated with data obtained from a 21 day Daphnia magna reproduction test with a reference substance, 3,4-dichloroaniline (3,4-DCA), and with a real effluent. Copyright © 2002 John Wiley & Sons, Ltd. [source]

Tail-dependence in stock-return pairs

INTELLIGENT SYSTEMS IN ACCOUNTING, FINANCE & MANAGEMENT, Issue 2 2002
Ines Fortin
The empirical joint distribution of return pairs on stock indices displays high tail-dependence in the lower tail and low tail-dependence in the upper tail. The presence of tail-dependence is not compatible with the assumption of (conditional) joint normality. The presence of asymmetric tail-dependence is not compatible with the assumption of a joint student-t distribution. A general test for one dependence structure versus another via the profile likelihood is described and employed in a bivariate GARCH model, where the joint distribution of the disturbances is split into its marginals and its copula. The copula used in the paper is such that it allows for the existence of lower tail-dependence and for asymmetric tail-dependence, and is such that it encompasses the normal or t-copula, depending on the benchmark tested. The model is estimated using bivariate data on a set of European stock indices. We find that the assumption of normal or student-t dependence is easily rejected in favour of an asymmetrically tail-dependent distribution. Copyright © 2002 John Wiley & Sons, Ltd. [source]

A note on the prospective analysis of outcome-dependent samples

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 2 2003
Hua Yun Chen
Summary. Two likelihood representations corresponding to the prospective and retrospective analyses of the case,control design are derived for general outcome-dependent samples with arbitrary discrete or continuous outcomes and possibly non-multiplicative models. Parameter identification in the general outcome-dependent design is reduced to the simple problem of parameter identification in the general odds ratio function. Both likelihoods are shown to generate the same profile likelihood for the common parameter of interest. Maximum like- lihood estimators based on either likelihood are semiparametric efficient for the identifiable parameters. [source]

LEVERAGE ADJUSTMENTS FOR DISPERSION MODELLING IN GENERALIZED NONLINEAR MODELS

AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 4 2009
Gordon K. Smyth
Summary For normal linear models, it is generally accepted that residual maximum likelihood estimation is appropriate when covariance components require estimation. This paper considers generalized linear models in which both the mean and the dispersion are allowed to depend on unknown parameters and on covariates. For these models there is no closed form equivalent to residual maximum likelihood except in very special cases. Using a modified profile likelihood for the dispersion parameters, an adjusted score vector and adjusted information matrix are found under an asymptotic development that holds as the leverages in the mean model become small. Subsequently, the expectation of the fitted deviances is obtained directly to show that the adjusted score vector is unbiased at least to,O(1/n). Exact results are obtained in the single-sample case. The results reduce to residual maximum likelihood estimation in the normal linear case. [source]

Semiparametric Estimation Exploiting Covariate Independence in Two-Phase Randomized Trials

BIOMETRICS, Issue 1 2009
James Y. Dai
Summary Recent results for case,control sampling suggest when the covariate distribution is constrained by gene-environment independence, semiparametric estimation exploiting such independence yields a great deal of efficiency gain. We consider the efficient estimation of the treatment,biomarker interaction in two-phase sampling nested within randomized clinical trials, incorporating the independence between a randomized treatment and the baseline markers. We develop a Newton,Raphson algorithm based on the profile likelihood to compute the semiparametric maximum likelihood estimate (SPMLE). Our algorithm accommodates both continuous phase-one outcomes and continuous phase-two biomarkers. The profile information matrix is computed explicitly via numerical differentiation. In certain situations where computing the SPMLE is slow, we propose a maximum estimated likelihood estimator (MELE), which is also capable of incorporating the covariate independence. This estimated likelihood approach uses a one-step empirical covariate distribution, thus is straightforward to maximize. It offers a closed-form variance estimate with limited increase in variance relative to the fully efficient SPMLE. Our results suggest exploiting the covariate independence in two-phase sampling increases the efficiency substantially, particularly for estimating treatment,biomarker interactions. [source]

Joint Analysis of Time-to-Event and Multiple Binary Indicators of Latent Classes

BIOMETRICS, Issue 1 2004
Klaus Larsen
Summary. Multiple categorical variables are commonly used in medical and epidemiological research to measure specific aspects of human health and functioning. To analyze such data, models have been developed considering these categorical variables as imperfect indicators of an individual's "true" status of health or functioning. In this article, the latent class regression model is used to model the relationship between covariates, a latent class variable (the unobserved status of health or functioning), and the observed indicators (e.g., variables from a questionnaire). The Cox model is extended to encompass a latent class variable as predictor of time-to-event, while using information about latent class membership available from multiple categorical indicators. The expectation-maximization (EM) algorithm is employed to obtain maximum likelihood estimates, and standard errors are calculated based on the profile likelihood, treating the nonparametric baseline hazard as a nuisance parameter. A sampling-based method for model checking is proposed. It allows for graphical investigation of the assumption of proportional hazards across latent classes. It may also be used for checking other model assumptions, such as no additional effect of the observed indicators given latent class. The usefulness of the model framework and the proposed techniques are illustrated in an analysis of data from the Women's Health and Aging Study concerning the effect of severe mobility disability on time-to-death for elderly women. [source]

Survival of Bowhead Whales, Balaena mysticetus, Estimated from 1981,1998 Photoidentification Data

BIOMETRICS, Issue 4 2002
Judith Zeh
Summary. Annual survival probability of bowhead whales, Balaena mysticetus, was estimated using both Bayesian and maximum likelihood implementations of Cormack and Jolly-Seber (JS) models for capture-recapture estimation in open populations and reduced-parameter generalizations of these models. Aerial photographs of naturally marked bowheads collected between 1981 and 1998 provided the data. The marked whales first photographed in a particular year provided the initial ,capture' and ,release' of those marked whales and photographs in subsequent years the ,recaptures'. The Cormack model, often called the Cormack-Jolly-Seber (CJS) model, and the program MARK were used to identify the model with a single survival and time-varying capture probabilities as the most appropriate for these data. When survival was constrained to be one or less, the maximum likelihood estimate computed by MARK was one, invalidating confidence interval computations based on the asymptotic standard error or profile likelihood. A Bayesian Markov chain Monte Carlo (MCMC) implementation of the model was used to produce a posterior distribution for annual survival. The corresponding reduced-parameter JS model was also fit via MCMC because it is the more appropriate of the two models for these photoidentification data. Because the CJS model ignores much of the information on capture probabilities provided by the data, its results are less precise and more sensitive to the prior distributions used than results from the JS model. With priors for annual survival and capture probabilities uniform from 0 to 1, the posterior mean for bowhead survival rate from the JS model is 0.984, and 95% of the posterior probability lies between 0.948 and 1. This high estimated survival rate is consistent with other bowhead life history data. [source]