SEMIPARAMETRIC ESTIMATION OF THE ERROR DISTRIBUTION IN MULTIVARIATE REGRESSION USING COPULAS

A semiparametric method is developed to estimate the dependence parameter and the joint distribution of the error term in the multivariate linear regression model. The nonparametric part of the method treats the marginal distributions of the error term as unknown, and estimates them using suitable empirical distribution functions. Then the dependence parameter is estimated by either maximizing a pseudolikelihood or solving an estimating equation. It is shown that this estimator is asymptotically normal, and a consistent estimator of its large sample variance is given. A simulation study shows that the proposed semiparametric method is better than the parametric ones available when the error distribution is unknown, which is almost always the case in practice. It turns out that there is no loss of asymptotic efficiency as a result of the estimation of regression parameters. An empirical example on portfolio management is used to illustrate the method.     

CASE-DELETION DIAGNOSTICS FOR TEST STATISTICS IN MULTIVARIATE REGRESSION

Influence measures in multivariate regression analysis have been widely developed, especially through use of the case-deletion approach. However, there seem to be few accounts of the influence of observations on test statistics in hypothesis testing. This paper examines four common multivariate tests, namely the Wilks' ratio, Lawley-Hotelling trace, Pillai's trace and Roy's greatest root for testing a general linear hypothesis of the regression coefficients in multivariate regression. The influence of observations is measured using the case-deletion approach. The proposed diagnostic measures, except that of Roy's greatest root, can be expressed in terms of statistics without involving the actual deletion of observations. An illustrative example is given with satisfactory results.     

SOME SECOND ORDER ASYMPTOTICS IN NONLINEAR REGRESSION

Some asymptotics related to statistical curvature are studied from a geometric point of view. The method of stochastic expansion is used to study the second order information loss and some conditional inference for the least squares estimator in nonlinear regression in which the samples are independent but not necessarily identically distributed.     

CALCULATION OF MULTIVARIATE NORMAL PROBABILITIES—ANOTHER SPECIAL CASE

The problem of calculating orthant probabilities for sets of variables (X₁,., X_n) is considered in the case where they are jointly normally distributed with zero means and a correlation matrix such that the correlation between X_i and X_i is zero if |i-j|> 1. An effective method is given which works for quite large n when the correlations between X_i and X_i+1 have the values 1/2, 2/5, 3/10, 4/17, 5/26,. and more approximate methods are given for other values. The accuracy is investigated numerically.     

SEMIPARAMETRIC COMPARISON OF REGRESSION CURVES VIA NORMAL LIKELIHOODS

Härdle & Marron (1990) treated the problem of semiparametric comparison of nonparametric regression curves by proposing a kernel-based estimator derived by minimizing a version of weighted integrated squared error. The resulting estimators of unknown transformation parameters are n-consistent, which prompts a consideration of issues. of optimality. We show that when the unknown mean function is periodic, an optimal nonparametric estimator may be motivated by an elegantly simple argument based on maximum likelihood estimation in a parametric model with normal errors. Strikingly, the asymptotic variance of an optimal estimator of θ does not depend at all on the manner of estimating error variances, provided they are estimated n-consistently. The optimal kernel-based estimator derived via these considerations is asymptotically equivalent to a periodic version of that suggested by Härdle & Marron, and so the latter technique is in fact optimal in this sense. We discuss the implications of these conclusions for the aperiodic case.     

MULTIVARIATE TESTS OF NORMAL MEAN VECTORS WITH RESTRICTED ALTERNATIVES

ABSTRACT

In this paper, we consider tests for the hypothesis that the mean vector is zero against one-sided alternatives when the observation vectors are independently and identically distributed as normal with unknown covariance matrix. The exact null-distribution of the tests is derived. The tests generalize the centre-direction test proposed by Tang et al.[1] Tang, D.-I., Gnecco, C. and Geller, N. 1989. An Approximate Likelihood Ratio Test for a Normal Mean Vector with Nonnegative Components with Application to Clinical Trials. Biometrika, 76: 577–583. [Crossref], [Web of Science ®] , [Google Scholar] for known covariance. In addition, the modification is order- and scale-invariant. Power comparisons with some other tests are presented. It can be shown that the null distribution of the test statistic holds for data arising from any elliptical distribution, not just the normal distribution.     

THE EXAMINATION AND ANALYSIS OF RESIDUALS FOR SOME BIASED ESTIMATORS IN LINEAR REGRESSION

In the presence of collinearity certain biased estimation procedures like ridge regression, generalized inverse estimator, principal component regression, Liu estimator, or improved ridge and Liu estimators are used to improve the ordinary least squares (OLS) estimates in the linear regression model. In this paper new biased estimator (Liu estimator), almost unbiased (improved) Liu estimator and their residuals will be analyzed and compared with OLS residuals in terms of mean-squared error.     

THE MONTE CARLO EVALUATION OF ORTHANT PROBABILITIES FOR MULTIVARIATE NORMAL DISTRIBUTIONS

The calculation of multivariate normal orthant probabilities is practically impossible when the number of variates is greater than five or six, except in very special cases. A transformation of the integral is obtained which enables quite accurate Monte Carlo estimates to be obtained for a fairly high number of dimensions, particularly if control variates are used.     

ON THE DISTRIBUTION OF THE REGRESSION COEFFICIENT MATRIX OF A NORMAL DISTRIBUTION

In this paper we generalize a result of Kshirsagar's (1960, pp. 83–84) on the distribution of the regression coefficient matrix for a multivariate normal population.     

EXPECTATIONS OF RATIOS OF QUADRATIC FORMS IN NORMAL VARIABLES: EVALUATING SOME TOP-ORDER INVARIANT POLYNOMIALS

Chikuse's (1987) algorithm constructs top-order invariant polynomials with multiple matrix arguments. Underlying it is a set of simultaneous equations for which all integer solutions must be found. Each solution represents a component of the sum of terms which comprise the polynomial. The system of equations has a specialised structure which may be exploited to obtain a polynomial with r matrix arguments in terms of a polynomial with r-1 matrix arguments. This is demonstrated for two particular polynomials that have two matrix arguments. These results are applied to problems involving expectations of ratios of quadratic forme in normal variables; analytic as well as computable formulae are derived.     

INFLUENCE FUNCTION OF THE LIKELIHOOD RATIO TEST STATISTIC FOR MULTIVARIATE NORMAL SAMPLE

ABSTRACT

We derive the influence function of the likelihood ratio test statistic for multivariate normal sample. The derived influence function does not depend on the influence functions of the parameters under the null hypothesis. So we can obtain directly the empirical influence function with only the maximum likelihood estimators under the null hypothesis. Since the derived formula is a general form, it can be applied to influence analysis on many statistical testing problems.     

MINIMAX ESTIMATION OF A MULTIVARIATE NORMAL MEAN UNDER A CONVEX LOSS FUNCTION

Let X = (X₁, - X_p)prime; ˜ N_p (μ, Σ) where μ= (μ₁, -, μ_p)' and Σ= diag (Σ²₁, -, Σ²_p) are both unknown and p3. Let (n_i - 2) w_i/Σ²_i! X²_ni, independent. of w_i (I ≠ j = 1, -, p). Assume that (w₁, -, w_p) and X are independent. Define W = diag (w₁, -, w_p) and ¶ X ¶²_w= X'W^-1Q^-1W^-1X where Q = diag (q₁, -,n q_p), q_i > 0, i = 1, -, p. In this paper, the minimax estimator of Berger & Bock (1976), given by δ (X, W) = [I_p - r(X, W) ¶ X ¶^-2_w Q^-1W^-1] X, is shown to be minimax relative to the convex loss (δ - μ)'[αQ + (1 - α) Σ^-1] δ - μ)/C, where C =α tr (Σ) + (1 - α)p and 0 α 1, under certain conditions on r(X, W). This generalizes the above mentioned result of Berger & Bock.     

SOME RESULTS ON TRUNCATION DEPENDENCE INVARIANT CLASS OF COPULAS

ABSTRACT

In this paper, m-dimensional distribution functions with truncation invariant dependence structure are studied. Some of the properties of generalized Archimedean class of copulas under this dependence structure are presented including some results on the conditions of compatibility. It has been shown that Archimedean copula generalized as it is described by Jouini and Clemen[1] Jouini, M.N. and Clemen, R.T. 1996. Copula Models for Aggregating Expert Opinions. Operations Research, 44(3): 444–457.  [Google Scholar] which has the truncation invariant dependence structure has to have the form of independence or Cook-Johnson copula. We also consider a multi-parameter class of copulas derived from one-parameter Archimedean copulas. It has been shown that this class has a probabilistic meaning as a connecting copula of the truncated random pair with a right truncation region on the third variable. Multi-parameter copulas generated in this paper stays in the Archimedean class. We provide formulas to compute Kendall's tau and explore the dependence behavior of this multi-parameter class through examples.     

SOME THEORY OF SAMPLING ON SUCCESSIVE OCCASIONS1

One of the important theoretical developments in successive sampling has been to provide an optimum estimate by combining two independent estimates (i) a double-sampling regression estimate from the matched portion of the sample using one auxiliary variable with (ii) a mean per unit estimate based on the unmatched portion of the sample. Theory has been generalized in the present paper to provide the optimum estimate by combining a double-sampling multivariate ratio or regression estimate using p auxiliary variables (p≥1) from the matched portion of the sample with a mean per unit estimate from the unmatched portion of the sample. Results have been presented for the more general and practical case when the samples on the two occasions are of unequal size.     

LOCATION OF PARTICULAR POINTS IN NON-PARAMETRIC REGRESSION ANALYSIS

In the random-design non-parametric regression model, the locations of particular values of the regression function or its derivatives are estimated. This paper investigates several stochastic modes of convergence and finds their rate of convergence under regularity assumptions, for a wide class of non-parametric estimators. The approach finds two natural fields of application: estimation of zeros/extrema and non-parametric absolute calibration.