Home  About us  Contact
 

Asymptotic Inference (asymptotic + inference)

Distribution by Scientific Domains
Mathematics and Statistics80%

Selected Abstracts

Generalized Birnbaum-Saunders distributions applied to air pollutant concentration

ENVIRONMETRICS, Issue 3 2008
Víctor Leiva
Abstract The generalized Birnbaum-Saunders (GBS) distribution is a new class of positively skewed models with lighter and heavier tails than the traditional Birnbaum-Saunders (BS) distribution, which is largely applied to study lifetimes. However, the theoretical argument and the interesting properties of the GBS model have made its application possible beyond the lifetime analysis. The aim of this paper is to present the GBS distribution as a useful model for describing pollution data and deriving its positive and negative moments. Based on these moments, we develop estimation and goodness-of-fit methods. Also, some properties of the proposed estimators useful for developing asymptotic inference are presented. Finally, an application with real data from Environmental Sciences is given to illustrate the methodology developed. This example shows that the empirical fit of the GBS distribution to the data is very good. Thus, the GBS model is appropriate for describing air pollutant concentration data, which produces better results than the lognormal model when the administrative target is determined for abating air pollution. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Emissions of greenhouse gases attributable to the activities of the land transport: modelling and analysis using I-CIR stochastic diffusion,the case of Spain

ENVIRONMETRICS, Issue 2 2008
R. Gutiérrez
Abstract In this study, carried out on the basis of the conclusions and methodological recommendations of the Fourth Assessment Report (2007) of the International Panel on Climate Change (IPCC), we consider the emissions of greenhouse gases (GHG), and particularly those of CO2, attributable to the activities of land transport, for all sectors of the economy, as these constitute a significant proportion of total GHG emissions. In particular, the case of Spain is an example of a worrying situation in this respect, both in itself and in the context of the European Union. To analyse the evolution, in this case, of such emissions, to enable medium-term forecasts to be made and to obtain a model that will enable us to analyse the effects of possible corrector mechanisms, we have statistically fitted a inverse Cox-Ingersoll-Ross (I-CIR) type nonlinear stochastic diffusion process, on the basis of the real data measured for the period 1990,2004, during which the Kyoto protocol has been applicable. We have studied the evolution of the trend of these emissions using estimated trend functions, for which purpose probabilistic complements such as trend functions and stationary distribution are incorporated, and a statistical methodology (estimation and asymptotic inference) for this diffusion, these tools being necessary for the application of the analytical methodology proposed. Copyright © 2007 John Wiley & Sons, Ltd. [source]

Multivariate tests comparing binomial probabilities, with application to safety studies for drugs

JOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 4 2005
Alan Agresti
Summary., In magazine advertisements for new drugs, it is common to see summary tables that compare the relative frequency of several side-effects for the drug and for a placebo, based on results from placebo-controlled clinical trials. The paper summarizes ways to conduct a global test of equality of the population proportions for the drug and the vector of population proportions for the placebo. For multivariate normal responses, the Hotelling T2 -test is a well-known method for testing equality of a vector of means for two independent samples. The tests in the paper are analogues of this test for vectors of binary responses. The likelihood ratio tests can be computationally intensive or have poor asymptotic performance. Simple quadratic forms comparing the two vectors provide alternative tests. Much better performance results from using a score-type version with a null-estimated covariance matrix than from the sample covariance matrix that applies with an ordinary Wald test. For either type of statistic, asymptotic inference is often inadequate, so we also present alternative, exact permutation tests. Follow-up inferences are also discussed, and our methods are applied to safety data from a phase II clinical trial. [source]

Blockwise generalized empirical likelihood inference for non-linear dynamic moment conditions models

THE ECONOMETRICS JOURNAL, Issue 2 2009
Francesco Bravo
Summary, This paper shows how the blockwise generalized empirical likelihood method can be used to obtain valid asymptotic inference in non-linear dynamic moment conditions models for possibly non-stationary weakly dependent stochastic processes. The results of this paper can be used to construct test statistics for overidentifying moment restrictions, for additional moments, and for parametric restrictions expressed in mixed implicit and constraint form. Monte Carlo simulations seem to suggest that some of the proposed test statistics have competitive finite sample properties. [source]

Small-Sample Inference for Incomplete Longitudinal Data with Truncation and Censoring in Tumor Xenograft Models

BIOMETRICS, Issue 3 2002
Ming Tan
Summary. In cancer drug development, demonstrating activity in xenograft models, where mice are grafted with human cancer cells, is an important step in bringing a promising compound to humans. A key outcome variable is the tumor volume measured in a given period of time for groups of mice given different doses of a single or combination anticancer regimen. However, a mouse may die before the end of a study or may be sacrificed when its tumor volume quadruples, and its tumor may be suppressed for some time and then grow back. Thus, incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumor shrinkage (<0.01 cm3) or random truncation. Because of the small sample sizes in these models, asymptotic inferences are usually not appropriate. We propose two parametric test procedures based on the EM algorithm and the Bayesian method to compare treatment effects among different groups while accounting for informative censoring. A real xenograft study on a new antitumor agent, temozolomide, combined with irinotecan is analyzed using the proposed methods. [source]