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Cox Transformation (cox + transformation)

Distribution by Scientific Domains
Mathematics and Statistics60%

Selected Abstracts

Assessing household health expenditure with Box,Cox censoring models

HEALTH ECONOMICS, Issue 9 2005
Jean-Paul Chaze
Abstract In order to assess the combined presence of zero expenditures and a heavily skewed distribution of positive expenditures, the Box,Cox transformation with location parameter is used to define a set of models generalising the standard Tobit, Heckman selection and double-hurdle models. Extended flexibility with respect to previous specifications is introduced, notably regarding negative transformation parameters, which may prove necessary for medical expenditures, and corner-solution outcomes. An illustration is provided by the analysis of household health expenditure in Switzerland. Copyright © 2005 John Wiley & Sons, Ltd. [source]

Using a Box,Cox transformation in the analysis of longitudinal data with incomplete responses

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 3 2000
S. R. Lipsitz
We analyse longitudinal data on CD4 cell counts from patients who participated in clinical trials that compared two therapeutic treatments: zidovudine and didanosine. The investigators were interested in modelling the CD4 cell count as a function of treatment, age at base-line and disease stage at base-line. Serious concerns can be raised about the normality assumption of CD4 cell counts that is implicit in many methods and therefore an analysis may have to start with a transformation. Instead of assuming that we know the transformation (e.g. logarithmic) that makes the outcome normal and linearly related to the covariates, we estimate the transformation, by using maximum likelihood, within the Box,Cox family. There has been considerable work on the Box,Cox transformation for univariate regression models. Here, we discuss the Box,Cox transformation for longitudinal regression models when the outcome can be missing over time, and we also implement a maximization method for the likelihood, assumming that the missing data are missing at random. [source]

A Box,Cox Double-hurdle Model

THE MANCHESTER SCHOOL, Issue 2 2000
Andrew M. Jones
The double-hurdle model with dependence is extended by incorporating the Box,Cox transformation. The model nests a range of popular limited dependent variable models, including the Gaussian double-hurdle, the generalized Tobit, and two-part models. Estimates of US beef consumption suggest that the Box,Cox specification outperforms all other restrictive models. Price elasticities are small and similar to findings in the literature. Household age composition and demographic variables also play significant roles in determining beef consumption. Income and cross-price elasticities are insignificant. [source]

Inducing normality from non-Gaussian long memory time series and its application to stock return data

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 4 2010
Kyungduk Ko
Abstract Motivated by Lee and Ko (Appl. Stochastic Models. Bus. Ind. 2007; 23:493,502) but not limited to the study, this paper proposes a wavelet-based Bayesian power transformation procedure through the well-known Box,Cox transformation to induce normality from non-Gaussian long memory processes. We consider power transformations of non-Gaussian long memory time series under the assumption of an unknown transformation parameter, a situation that arises commonly in practice, while most research has been devoted to non-linear transformations of Gaussian long memory time series with known transformation parameter. Specially, this study is mainly focused on the simultaneous estimation of the transformation parameter and long memory parameter. To this end, posterior estimations via Markov chain Monte Carlo methods are performed in the wavelet domain. Performances are assessed on a simulation study and a German stock return data set. Copyright © 2009 John Wiley & Sons, Ltd. [source]