791 research outputs found
Location-adjusted Wald statistics for scalar parameters
No full textInference about a scalar parameter of interest is a core statistical task that has attracted immense research in statistics. The Wald statistic is a prime candidate for the task, on the grounds of the asymptotic validity of the standard normal approximation to its finite-sample distribution, simplicity and low computational cost. It is well known, though, that this normal approximation can be inadequate, especially when the sample size is small or moderate relative to the number of parameters. A novel, algebraic adjustment to the Wald statistic is proposed, delivering significant improvements in inferential performance with only small implementation and computational overhead, predominantly due to additional matrix multiplications. The Wald statistic is viewed as an estimate of a transformation of the model parameters and is appropriately adjusted, using either maximum likelihood or reduced-bias estimators, bringing its expectation asymptotically closer to zero. The location adjustment depends on the expected information, an approximation to the bias of the estimator, and the derivatives of the transformation, which are all either readily available or easily obtainable in standard software for a wealth of models. An algorithm for the implementation of the location-adjusted Wald statistics in general models is provided, as well as a bootstrap scheme for the further scale correction of the location-adjusted statistic. Ample analytical and numerical evidence is presented for the adoption of the location-adjusted statistic in prominent modelling settings, including inference about log-odds and binomial proportions, logistic regression in the presence of nuisance parameters, beta regression, and gamma regression. The location-adjusted Wald statistics are used for the construction of significance maps for the analysis of multiple sclerosis lesions from MRI data
On iterative adjustment of responses for the reduction of bias in binary regression models
No full textThe adjustment of the binomial data by small constants is a common practice in statistical
modelling, for avoiding sparseness issues and, historically, for improving the asymptotic properties
of the estimators. However, there are two main disadvantages with such practice: i) there
is not a universal constant adjustment that results estimators with optimal asymptotic properties
for all possible modelling settings, and ii) the resultant estimators are not invariant to the
representation of the binomial data. In the current work, we present a parameter-dependent
adjustment scheme which is applicable to binomial-response generalized linear models with arbitrary
link functions. The adjustment scheme results by the expressions for the bias-reducing
adjusted score functions in Kosmidis & Firth (2008, Biometrika) and thus its use guarantees
estimators with second-order bias. Based on an appropriate expression of the adjusted data,
a procedure for obtaining the bias-reduced estimates is developed which relies on the iterative
adjustment of the binomial responses and totals using existing maximum likelihood implementations.
Furthermore, it is shown that the bias-reduced estimator, like the maximum likelihood
estimator, is invariant to the representation of the binomial data. A complete enumeration
study is used to demonstrate the superior statistical properties of the bias-reduced estimator to
the maximum likelihood estimator
Bias corrected z-tests for regression models
No full textIn regression settings the e ect of a covariate, accounting for all the others, on the dependent variable is typically tested by using a z-statistic. Under regularity conditions on the model and assuming the null hypothesis holds, the associated Wald pivot is asymptotically normally distributed. However, its nite- sample distribution can be far from Gaussian when the sample size is small or moderate relative to the dimension of the global parameter. In this work, asymptotic bias correction of the Wald z-statistic is proposed as a means to improve the accuracy of rst-order inference for the regression coe cients
Improving the accuracy of likelihood-based inference in meta-analysis and meta-regression
No full textRandom-effects models are frequently used to synthesise information from different studies in meta-analysis. While likelihood-based inference is attractive both in terms of limiting properties and of implementation, its application in random-effects meta-analysis may result in misleading conclusions, especially when the number of studies is small to moderate. The current paper shows how methodology that reduces the asymptotic bias of the maximum likelihood estimator of the variance component can also substantially improve inference about the mean effect size. The results are derived for the more general framework of random-effects meta-regression, which allows the mean effect size to vary with study-specific covariates
A generic algorithm for reducing bias in parametric estimation
No full textA general iterative algorithm is developed for the computation
of reduced-bias parameter estimates in regular statistical models through
adjustments to the score function. The algorithm unifies and provides appealing new interpretation for iterative methods that have been published
previously for some specific model classes. The new algorithm can usefully be viewed as a series of iterative bias corrections, thus facilitating the
adjusted score approach to bias reduction in any model for which the first-
order bias of the maximum likelihood estimator has already been derived.
The method is tested by application to a logit-linear multiple regression
model with beta-distributed responses; the results confirm the effectiveness
of the new algorithm, and also reveal some important errors in the existing
literature on beta regression
Supplementary_Table – Supplemental material for Prevalence and Risk Factors of Frailty in a Community-Dwelling Population: The HELIAD Study
No full textSupplemental material, Supplementary_Table for Prevalence and Risk Factors of Frailty in a Community-Dwelling Population: The HELIAD Study by E. Ntanasi, M. Yannakoulia, N. Mourtzi, G. S. Vlachos, M. H. Kosmidis, C. A. Anastasiou, E. Dardiotis, G. Hadjigeorgiou, M. Megalou, P. Sakka and N. Scarmeas in Journal of Aging and Health</p
Multinomial logit bias reduction via Poisson log-linear model
No full textIt is shown how to obtain the bias-reducing penalized maximum likelihood estimator for
the parameters of a multinomial logistic regression by using the equivalent Poisson log-linear
model. This allows a simple and computationally efficient implementation of the reduced-bias
estimator, using standard software for generalized linear models
Hypnotherapy for control of anticipatory nausea and vomiting in children with cancer: preliminary findings
No full text
Immaturity of human stem-cell-derived cardiomyocytes in culture: fatal flaw or soluble problem?
No full textCardiomyocytes from human pluripotent stem cells (hPSC-CMs) are increasingly used to model cardiac disease, test drug efficacy and for safety pharmacology. Nevertheless, a major hurdle to more extensive use is their immaturity and similarity to fetal rather than adult cardiomyocytes. Here, we provide an overview of the strategies currently being used to increase maturation in culture, which include prolongation of time in culture, exposure to electrical stimulation, application of mechanical strain, growth in three-dimensional tissue configuration, addition of non-cardiomyocytes, use of hormones and small molecules, and alteration of the extracellular environment. By comparing the outcomes of these studies, we identify the approaches most likely to improve functional maturation of hPSC-CMs in terms of their electrophysiology and excitation-contraction couplin
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