A note on the residual median process

We study the residual median process, defined as the median of those observations which are greater than a number t. Using appropriate limit theorems, it is shown that the stochastic process converges in law to a Gaussian process defined in terms of a Brownian bridge.     

A note on the asymptotic behaviour of empirical likelihood statistics

This paper develops some theoretical results about the asymptotic behaviour of the empirical likelihood and the empirical profile likelihood statistics, which originate from fairly general estimating functions. The results accommodate, within a unified framework, various situations potentially occurring in a wide range of applications. For this reason, they are potentially useful in several contexts, such as, for example, in inference for dependent data. We provide examples showing that known findings in literature about the asymptotic behaviour of some empirical likelihood statistics in time series models can be derived as particular cases of our results.     

A note on the two-sample mean problem based on jackknife empirical likelihood

In this article, we employ the jackknife empirical likelihood (JEL) method to construct the confidence regions for the difference of the means of two d-dimensional samples. Compared with traditional EL for the two-sample mean problem, JEL is extremely simpler to use in practice and is more effective in computing. Based on the JEL ratio test, a version of Wilks’ theorem is developed. Furthermore, to improve the coverage accuracy of confidence regions, a Bartlett correction is applied. The effectiveness of the proposed method is demonstrated by a simulation study and a real data analysis.     

A note on millers's empirical weights for heteroscedastic linear regression

For heteroscedastic simple linear regression when the variances are proportional to a power of the mean of the response variable, Miller (1986) recommends the following procedure: do a weighted least squares regression with the weights (empirical weights) estimated by the inverse of the appropriate power of the response variable. The practical appeal of this approach is its simplicity.

In this article some of the consequences of this simple procedure are considered. Specifically, the effect of this procedure on the bias of the point estimators of the regression coefficients and on the coverage probabilities of their corresponding confidence intervals is examined. It is found that the performance of the process of employing empirical weights in a weighted least squares regression depends on : (1) the particular regression parameter (slope or intercept) of interest, (2) the appropriate power of the mean of the response variable involved, and (3) the amount of variation in the data about the true regression line.     

A note on empirical likelihoods derived from pairwise score functions

Pairwise likelihood functions are convenient surrogates for the ordinary likelihood, useful when the latter is too difficult or even impractical to compute. One drawback of pairwise likelihood inference is that, for a multidimensional parameter of interest, the pairwise likelihood analogue of the likelihood ratio statistic does not have the standard chi-square asymptotic distribution. Invoking the theory of unbiased estimating functions, this paper proposes and discusses a computationally and theoretically attractive approach based on the derivation of empirical likelihood functions from the pairwise scores. This approach produces alternatives to the pairwise likelihood ratio statistic, which allow reference to the usual asymptotic chi-square distribution and which are useful when the elements of the Godambe information are troublesome to evaluate or in the presence of large data sets with relative small sample sizes. Two Monte Carlo studies are performed in order to assess the finite-sample performance of the proposed empirical pairwise likelihoods.     

A note on the scale parameter of the dirichlet process

This paper gives an interpretation for the scale parameter of a Dirichlet process when the aim is to estimate a linear functional of an unknown probability distribution. We provide exact first and second posterior moments for such functionals under both informative and noninformative prior specifications. The noninformative case provides a normal approximation to the Bayesian bootstrap.     

A note on maximum likelihood estimation for the first-order autoregressive process

The maximum likelihood estimator of the parameters of a zero-mean normal stationary first-order autoregressive process is in-vestigated. it is shown that the likelihood function is uniquely maximized at a point in the interior of the parameter space. A closed-form expression is obtained for the estimator.     

A note on the asymptotic generalised variance for a moving average process

Relationships between the asymptotic generalised variance, of an r^th order moving average process, and the ordinary variance, of an associated r^th order autoregressive process, are established for r=1 and 2. This is extended to the case r=3, and a generalisation is suggested for all r>O, though this has not been checked beyond r=4.     

A note on the asymptotic distribution of the process capability index C pmk

Process capability indices have been widely used to evaluate the process performance to the continuous improvement of quality and productivity. The distribution of the estimator of the process capability index C _pmk is very complicated and the asymptotic distribution is proposed by Chen and Hsu [The asymptotic distribution of the processes capability index C _pmk , Comm. Statist. Theory Methods 24(5) (1995), pp. 1279–1291]. However, we found a critical error for the asymptotic distribution when the population mean is not equal to the midpoint of the specification limits. In this paper, a correct version of the asymptotic distribution is given. An asymptotic confidence interval of C _pmk by using the correct version of asymptotic distribution is proposed and the lower bound can be used to test if the process is capable. A simulation study of the coverage probability of the proposed confidence interval is shown to be satisfactory. The relation of six sigma technique and the index C _pmk is also discussed in this paper. An asymptotic testing procedure to determine if a process is capable based on the index of C _pmk is also given in this paper.     

A note on the convergence rates for empirical bayes estimators of parameters in multiple-parameter exponential families

Under suitable conditions upon prior distribution, the convergence rates for empirical Bayes estimators of parameters in multi-parameter exponential families (M-PEF) are obtained. It is shown that the assumptions Tong (1996) imposed on the marginal density can be reduced. The above result can also be extended to more general forms of M-PEF. Finally, some examples which satisfy the conditions of the theorems are given.     

Consistency of the Subsample Bootstrap empirical process

In the classical Bootstrap approach the number of distinct observation in the resample is random. To overcome this hitch Rao et al. [Bootstrap by sequential resampling, J. Statist. Plan. Inference 64 (1997), pp. 257–281] have proposed a modified resampling procedure – the so-called Sequential Bootstrap or 0.632-Bootstrap – in which each resample has exactly the same number m ～eq ?0.632 n? of distinct observations. Motivated by this idea we introduce an akin procedure, the Subsample Bootstrap, where additionally even the size of each resample is equal. It will turn out that the Subsample Bootstrap empirical process is consistent for a wide class of Donsker classes.     

A consistent bootstrapped GMM estimator for the linear model with arbitrary inequality constraints on parameters

The paper provides an estimation procedure for the linear model with arbitrary constraints on parameters. The consistency of the both the regular GMM and the bootstrapped GMM estimators for this model is shown. The estimation is formulated and solved as an iterative NLP problem with inequality constraints.     

Theoretic and empirical data-inclusive process characterization

Summary.  In process characterization the quality of information that is obtained depends directly on the quality of process model. The current quality revolution is now providing a strong stimulus for rethinking and re-evaluating many statistical ideas. Among these are the role of theoretic knowledge and data in statistical inference and some issues in theoretic–empirical modelling. With this concern the paper takes a broad, pragmatic view of statistical inference to include all aspects of model formulation. The estimation of model parameters traditionally assumes that a model has a prespecified known form and takes no account of possible uncertainty regarding model structure. But in practice model structural uncertainty is a fact of life and is likely to be more serious than other sources of uncertainty which have received far more attention. This is true whether the model is specified on subject-matter grounds or when a model is formulated, fitted and checked on the same data set in an iterative interactive way. For that reason novel modelling techniques have been fashioned for reducing model uncertainty. Using available knowledge for theoretic model elaboration the techniques that have been created approximate the exact unknown process model concurrently by accessible theoretic and polynomial empirical functions. The paper examines the effects of uncertainty for hybrid theoretic–empirical models and, for reducing uncertainty, additive and multiplicative methods of model formulation are fashioned. Such modelling techniques have been successfully applied to perfect a steady flow model for an air gauge sensor. Validation of the models elaborated has revealed that the multiplicative modelling approach allows us to attain a satisfactory model with small discrepancy from empirical evidence.     

A note on the asymptotic and exact Fisher information matrices of a Markov switching VARMA process

Statistical Methods & Applications - We study the asymptotic and exact Fisher information (FI) matrices of Markov switching vector autoregressive moving average (MS VARMA) models. In a related...     

A note on the Cook''s distance

A modification of the classical Cook's distance is proposed, providing us with a generalized Mahalanobis distance in the context of multivariate elliptical linear regression models. We establish the exact distribution of a pivotal type statistics based on this generalized Mahalanobis distance, which provides critical points for the identification of outlier data points. Based on the equivalence between the modified Cook's distance and what is called the mean-shift multivariate outlier elliptical model, twelve new modifications are proposed for the Cook's distance. We also describe the explicit relationship between the Cook's distance and the likelihood displacement with the modified Cook's distance. We illustrate the procedure with some examples, in the context of multiple and multivariate linear regression.     

An empirical note concerning systematic measurement errors in regression analysis

The bias in the estimated coefficient of an explanatory variable in a regression equation because of a systematic measurement error in another explanatory variable is considered. A general expression for bias is set forth. An actual problem is used as a case study in which the magnitude of the bias in an estimated price coefficient is evaluated using real data.