A sequential testing procedure for outliers and structural change

In this paper a test for outliers based on externally studentized residuals is shown to be related to a test for predictive failure. The relationships between a test for outliers, a test for a correlated mean shift and a test for an intercept shift are developed. A sequential testing procedure for outliers and structural change is shown to be independent, so that the overall size of the joint test can be determined exactly. It is established that a joint test for outliers and constancy of variances cannot be performed.     

玉米氮、磷配施经济效益分析

本文运用报酬递减律原理 ,对配方施肥进行效益最优化定量研究     

A monte carlo study of some multivariate nonparametric statistics for profile analysis of several samples †

This paper describes the results of simulation studies on a class of nonparametric tests,T ₀, developed for the hypothesis of homogeneityH ₀, of several multivariate populations, and also on the class of testsT ₁, developed subsequently for the hypothesisH ₁, of parallelism of population profiles. Simulation studies relate to, first, the investigation of accuracy of the large-sample null-approximation for finite samples and, secondly, the study of powers under various types of alternatives to H ₀ and H ₁.     

REDUCED DISTANCE MEASURES AND TRANSFORMATIONS IN PROCESSING MULTIVARIATE OUTLIERS

The study of multivariate outliers raises many problems of definition, principle and manipulation. Well-authenticated tests of discordancy exist only for the multivariate normal distribution. Detection of outliers in non-normal distributions involves the adoption of appropriate criteria to represent 'extremeness' of observations in a sample; corresponding tests of discordancy usually require tedious, or even intractable, distributional and computational manipulations. A class of transformations of the data is considered with a view of transferring some of the familiar and desirable features of discordancy tests for normal samples to non-normal situations.     

Tests for the Null Hypothesis of Cointegration: A Monte Carlo Comparison

The aim of this paper is to compare the relative performance of several tests for the null hypothesis of cointegration, in terms of size and power in finite samples. This is carried out using Monte Carlo simulations for a range of plausible data-generating processes. We also analyze the impact on size and power of choosing different procedures to estimate the long run variance of the errors. We found that the parametrically adjusted test of McCabe et al. (1997) is the most well-balanced test, displaying good power and relatively few size distortions.     

Checks of model adequacy for univariate time series models and their application to econometric relationships

Tests derived from time series analysis play an important role in many empirical studies. These tests are frequently applied to the residuals obtained by fitting an econometric model using some standard estimator. We focus attention here on tests developed for univariate time series models. Various approaches to testing the adequacy of such models are discussed and compared. The validity and sefulness of applying these tests to econometric residuals are then examined and some Monte Carlo evidence is reported.     

A separability result for gmm estimation,with applications to gls prediction and conditional moment tests

Generalized method of moments estimates are unaffected by the addition of equal numbers of moment conditions and extra parameters. We prove thisresult and give examples of its use.     

Orthogonal Locally Ancillary Estimating Functions with Application to Stratified Clustered Data

We propose an orthogonal locally ancillary estimating function that provides first-order bias correction of inferences. It requires the specification of merely the first two moments of the observations when applying to analysis of stratified clustered (continuous or binary) data with the parameters of interest in both the first and second joint moments of dependent data. Simulation results confirm that the estimators obtained using the proposed method are substantially improved over those using regular profile estimating functions.     

A Direct Approach to Understanding Posterior Consistency of Bayesian Regression Problems

Previous approaches to establishing posterior consistency of Bayesian regression problems have used general theorems that involve verifying sufficient conditions for posterior consistency. In this article, we consider a direct approach by computing the posterior density explicitly and evaluating its asymptotic behavior. For this purpose, we deal with a sample size dependent prior based on a truncated regression function with increasing sample size, and evaluate the asymptotic properties of the resulting posterior. Based on a concept called posterior density consistency, we attempt to understand posterior consistency. As an application, we illustrate that the posterior density of an orthogonal semiparametric regression model is consistent.     

A Test of Independence in Two-Way Contingency Tables Based on Maximal Correlation

Maximal correlation has several desirable properties as a measure of dependence, including the fact that it vanishes if and only if the variables are independent. Except for a few special cases, it is hard to evaluate maximal correlation explicitly. We focus on two-dimensional contingency tables and discuss a procedure for estimating maximal correlation, which we use for constructing a test of independence. We compare the maximal correlation test with other tests of independence by Monte Carlo simulations. When the underlying continuous variables are dependent but uncorrelated, we point out some cases for which the new test is more powerful.