Third-order inference for autocorrelation in nonlinear regression models

Nguimkeu, Pierre; Rekkas, M.

doi:10.1016/j.jspi.2011.04.019

Cited by 7 publications

(9 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section we present the nonlinear regression model and describe a procedure that can be used to obtain highly accurate tail probabilities for testing first-order moving average disturbances in this model. We will follow the notation in Nguimkeu and Rekkas [1] (hereafter denoted by NR) as close as possible.…”

Section: The Model and The Proposed Methodsmentioning

confidence: 99%

“…These criteria are standard in the literature and have been considered, for example, by Fraser et al [18] and Chang and Wong [19]. The setup of the Monte Carlo simulation is the logistic growth model that is widely used in many applications (see [1,[20][21][22][23]). The model is defined by…”

Section: Monte Carlo Simulationsmentioning

confidence: 99%

“…Nguimkeu and Rekkas [1] proposed a third-order procedure to accurately test for autocorrelation in the disturbances, , of the nonlinear regression model (1) when only few data are available. In this paper we show how the same approach can be used to test for first-order moving average, MA (1), disturbances in such models. We therefore assume in the present framework that the disturbance term in Model (1) follows a first-order moving average process, MA(1),…”

Section: Introductionmentioning

confidence: 99%

“…In this paper we show how the same approach can be used to test for first-order moving average, MA (1), disturbances in such models. We therefore assume in the present framework that the disturbance term in Model (1) follows a first-order moving average process, MA(1),…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improved Inference for Moving Average Disturbances in Nonlinear Regression Models

Nguimkeu

2014

Journal of Probability and Statistics

View full text Add to dashboard Cite

This paper proposes an improved likelihood-based method to test for first-order moving average in the disturbances of nonlinear regression models. The proposed method has a third-order distributional accuracy which makes it particularly attractive for inference in small sample sizes models. Compared to the commonly used first-order methods such as likelihood ratio and Wald tests which rely on large samples and asymptotic properties of the maximum likelihood estimation, the proposed method has remarkable accuracy. Monte Carlo simulations are provided to show how the proposed method outperforms the existing ones. Two empirical examples including a power regression model of aggregate consumption and a Gompertz growth model of mobile cellular usage in the US are presented to illustrate the implementation and usefulness of the proposed method in practice.

show abstract

Section: The Model and The Proposed Methodsmentioning

confidence: 99%

Section: Monte Carlo Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Inference for Moving Average Disturbances in Nonlinear Regression Models

Nguimkeu

2014

Journal of Probability and Statistics

View full text Add to dashboard Cite

show abstract

“…We proceed by grid search, assuming that by the end of the epidemic, the final size of the epidemic would be a fraction

of the population thus setting the starting value of

. Then, using the same reasoning as in Nguimkeu and Rekkas (2011) , we can derive the starting value for

and that of

By varying the values of

, (e.g.

.)…”

Section: Empirical Analysismentioning

confidence: 99%

Early dynamics of transmission and projections of COVID-19 in some West African countries

Assob-Nguedia¹,

Dongo²,

Nguimkeu³

2020

Infectious Disease Modelling

View full text Add to dashboard Cite

The initial cases of novel coronavirus (COVID-19) were identified in most West African countries between late February and early March of 2020. But it is only after March 15, 2020 that the number of cases started rising significantly in these countries. This study analyzes the transmission dynamics of the outbreak in West Africa nearly 5 months after the effective onset. We focus on Cameroon, Ghana, Guinea and Nigeria, which are the four West African countries with the highest numbers of infected cases. We combine models of mathematical epidemiology and publicly available data to estimate the main disease transmission characteristics. In particular, we estimate the initial doubling time, the peak time, the peak rate, the final size and the short-term transmission forecasts of the COVID-19 epidemic for these countries. Policy implications for the effectiveness of control measures and for assessing the potential impact on public health in West Africa are discussed.

show abstract

Improved likelihood-based inference in Birnbaum–Saunders nonlinear regression models

Lemonte

Cordeiro

Moreno-Arenas

2016

Applied Mathematical Modelling

View full text Add to dashboard Cite

We address the issue of performing testing inference in Birnbaum-Saunders nonlinear regression models when the sample size is small. The likelihood ratio, Wald and score statistics provide the basis for testing inference on the parameters in this class of models. We focus on the small-sample case, where the reference chi-squared distribution gives a poor approximation to the true null distribution of these test statistics. We derive a general Bartlett-type correction in matrix notation for the score test, which reduces the size distortion of the test, and numerically compare the proposed test with the usual likelihood ratio, Wald and score tests, and with the Bartlett-corrected likelihood ratio test, and bootstrapcorrected tests. Our simulation results suggest that the proposed corrected test can be an interesting alternative to other tests since it leads to very accurate inference even for very small samples. We also present an empirical application for illustrative purposes.

show abstract

Third-order inference for autocorrelation in nonlinear regression models

Cited by 7 publications

References 17 publications

Improved Inference for Moving Average Disturbances in Nonlinear Regression Models

Improved Inference for Moving Average Disturbances in Nonlinear Regression Models

Early dynamics of transmission and projections of COVID-19 in some West African countries

Improved likelihood-based inference in Birnbaum–Saunders nonlinear regression models

Contact Info

Product

Resources

About