Simultaneous inferences for variance ratios of some mixed linear models

Earlier investigations used a one-sided inequality to consltuct confidence regions for the variance ratios or balanced randoiu models. In this study, confidence regions are based on a two-sided generalisation of this inequality and the results are illustrated by estimating the parameters of some elementary random models.     

Simultaneous confidence bands for nonlinear regression models

The maximization and minimization procedure for constructing confidence bands about general regression models is explained. Then, using an existing confidence region about the parameters of a nonlinear regression model and the maximization and minimization procedure, a generally conservative simultaneous confidence band is constructed about the model. Two examples are given, and some problems with the procedure are discussed     

A ray method of confidence band construction for multiple linear regression models

This paper addresses the problem of confidence band construction for a standard multiple linear regression model. A “ray” method of construction is developed which generalizes the method of Graybill and Bowden [1967. Linear segment confidence bands for simple linear regression models. J. Amer. Statist. Assoc. 62, 403–408] for a simple linear regression model to a multiple linear regression model. By choosing suitable directions for the rays this method requires only critical points from t-distributions so that the confidence bands are easy to construct. Both one-sided and two-sided confidence bands can be constructed using this method. An illustration of the new method is provided.     

Effective simultaneous confidence bands for repeated measurements in linear mixed-effect models

Linear mixed-effect (LME) models have been extensively accepted to analyse repeated measurements due to their flexibility and ability to handle subject-specific matters. The inclusion of random effects has resulted in much benefit with respect to estimation, but it is complicated to measure their impact on hypothesis testing. While the same complication is present in the construction of simultaneous confidence bands (SCBs), degrees of freedom (df) for SCBs have rarely been discussed unlike those for test statistics. This motivates us to propose the adoption of approximate df to construct SCBs in LME models. Simulation studies were performed to compare the performances of different calculations for the df. The results of simulations demonstrate the efficacy of the use of approximate df. In addition, our proposal allows line-segment SCBs developed under covariance models to function with LME models. Applications with real longitudinal datasets present consistent results with the simulation study.     

Simultaneous Confidence Bands for Linear Regression with Covariates Constrained in Intervals

Abstract. The focus of this article is on simultaneous confidence bands over a rectangular covariate region for a linear regression model with k>1 covariates, for which only conservative or approximate confidence bands are available in the statistical literature stretching back to Working & Hotelling (J. Amer. Statist. Assoc. 24 , 1929; 73–85). Formulas of simultaneous confidence levels of the hyperbolic and constant width bands are provided. These involve only a k‐dimensional integral; it is unlikely that the simultaneous confidence levels can be expressed as an integral of less than k‐dimension. These formulas allow the construction for the first time of exact hyperbolic and constant width confidence bands for at least a small k(>1) by using numerical quadrature. Comparison between the hyperbolic and constant width bands is then addressed under both the average width and minimum volume confidence set criteria. It is observed that the constant width band can be drastically less efficient than the hyperbolic band when k>1. Finally it is pointed out how the methods given in this article can be applied to more general regression models such as fixed‐effect or random‐effect generalized linear regression models.     

Non-nested linear models: A conditional confidence approach

The comparison of nested linear models with normal error is well standardized in the common procedures of the analysis of variance. This article considers the comparison of two non-nested linear models that have the same parameter dimension; the comparison is made on the assumption that the true mean lies somewhere in the linear span of the two models. The analysis leads to a precision-based conditional confidence interval for the unsigned angular direction of the true mean, and this in turn provides a confidence assessment of the two directions that correspond to the two models being compared. The confidence interval is an approximate conditional interval (given the distance of the estimate from the intersection of the hypotheses), and its length as a fraction of π indicates the precision of the confidence procedure. The method provides a conditional-inference alternative to a confidence interval available by Creasy-Fieller analysis.     

Confidence bands for the difference of two survival functions under the additive risk model

In many clinical studies, a commonly encountered problem is to compare the survival probabilities of two treatments for a given patient with a certain set of covariates, and there is often a need to make adjustments for other covariates that may affect outcomes. One approach is to plot the difference between the two subject-specific predicted survival estimates with a simultaneous confidence band. Such a band will provide useful information about when these two treatments differ and which treatment has a better survival probability. In this paper, we show how to construct such a band based on the additive risk model and we use the martingale central limit theorem to derive its asymptotic distribution. The proposed method is evaluated from a simulation study and is illustrated with two real examples.     

Simultaneous Confidence Tubes in Multivariate Linear Regression

Simultaneous confidence bands have been shown in the statistical literature as powerful inferential tools in univariate linear regression. While the methodology of simultaneous confidence bands for univariate linear regression has been extensively researched and well developed, no published work seems available for multivariate linear regression. This paper fills this gap by studying one particular simultaneous confidence band for multivariate linear regression. Because of the shape of the band, the word ‘tube’ is more pertinent and so will be used to replace the word ‘band’. It is shown that the construction of the tube is related to the distribution of the largest eigenvalue. A simulation‐based method is proposed to compute the 1 ? α quantile of this eigenvalue. With the computation power of modern computers, the simultaneous confidence tube can be computed fast and accurately. A real‐data example is used to illustrate the method, and many potential research problems have been pointed out.     

Exact uniformly most powerful postselection confidence distributions

A conditioning on the event of having selected one model from a set of possibly misspecified normal linear regression models leads to the construction of uniformly optimal conditional confidence distributions. They can be used for valid postselection inference. The constructed conditional confidence distributions are finite sample exact and encompass all information regarding the focus parameter in the selected model. This includes the construction of optimal postselection confidence intervals at all significance levels and uniformly most powerful hypothesis tests.     

Simultaneous pseudo confidence regions for ratios of the discriminant coefficients

We consider simultaneous confidence regions for some hypotheses on ratios of the discriminant coefficients of the linear discriminant function when the population means and common covariance matrix are unknown. This problem, involving hypotheses on ratios, yields the so-called ‘pseudo’ confidence regions valid conditionally in subsets of the parameter space. We obtain the explicit formulae of the regions and give further discussion on the validity of these regions. Illustrations of the pseudo confidence regions are given.     

Statistical estimation for partially linear error-in-variable models with error-prone covariates

Motivated by a heart disease data, we propose a new partially linear error-in-variable models with error-prone covariates, in which mismeasured covariate appears in the noparametric part and the covariates in the parametric part are not observed, but ancillary variables are available. In this case, we first calibrate the linear covariates, and then use the least-square method and the local linear method to estimate parametric and nonparametric components. Also, under certain conditions the asymptotic distributions of proposed estimates are obtained. Simulated and real examples are conducted to illustrate our proposed methodology.     

Simultaneous confidence intervals for one-way layout based on generalized pivotal quantities

In this paper, we consider simultaneous confidence intervals for all-pairwise comparisons of treatment means in a one-way layout under heteroscedasticity. Two kinds of simultaneous intervals are provided based on the fiducial generalized pivotal quantities of the interest parameters. We prove that they both have asymptotically correct coverage. Simulation results and an example are also reported. It is concluded from calculational evidence that the second kind of simultaneous confidence intervals, which we provide, performs better than existing methods.     

Classical testing in functional linear models

We extend four tests common in classical regression – Wald, score, likelihood ratio and F tests – to functional linear regression, for testing the null hypothesis, that there is no association between a scalar response and a functional covariate. Using functional principal component analysis, we re-express the functional linear model as a standard linear model, where the effect of the functional covariate can be approximated by a finite linear combination of the functional principal component scores. In this setting, we consider application of the four traditional tests. The proposed testing procedures are investigated theoretically for densely observed functional covariates when the number of principal components diverges. Using the theoretical distribution of the tests under the alternative hypothesis, we develop a procedure for sample size calculation in the context of functional linear regression. The four tests are further compared numerically for both densely and sparsely observed noisy functional data in simulation experiments and using two real data applications.     

Subset selection in multiple linear regression in the presence of outlier and multicollinearity

Various subset selection methods are based on the least squares parameter estimation method. The performance of these methods is not reasonably well in the presence of outlier or multicollinearity or both. Few subset selection methods based on the $M$-estimator are available in the literature for outlier data. Very few subset selection methods account the problem of multicollinearity with ridge regression estimator.In this article, we develop a generalized version of $S_p$ statistic based on the jackknifed ridge $M$-estimator for subset selection in the presence of outlier and multicollinearity. We establish the equivalence of this statistic with the existing $C_p$, $S_p$ and $R_p$ statistics. The performance of the proposed method is illustrated through some numerical examples and the correct model selection ability is evaluated using simulation study.     

Estimation of the common location paeameters for the two linear models

Various estimators proposed for the estimation of a common mean are extended to the estimation of the common location parameters for two linear models including the estimators based on preliminary tests of equality of variances. Exact distribution of these estimates, simultaneous confidence bounds based on these estimates and the bounds on the variances of these estimates are obtained using different approaches.     

The large sample coverage probability of confidence intervals in general regression models after a preliminary hypothesis test

We derive a computationally convenient formula for the large sample coverage probability of a confidence interval for a scalar parameter of interest following a preliminary hypothesis test that a specified vector parameter takes a given value in a general regression model. Previously, this large sample coverage probability could only be estimated by simulation. Our formula only requires the evaluation, by numerical integration, of either a double or a triple integral, irrespective of the dimension of this specified vector parameter. We illustrate the application of this formula to a confidence interval for the odds ratio of myocardial infarction when the exposure is recent oral contraceptive use, following a preliminary test where two specified interactions in a logistic regression model are zero. For this real‐life data, we compare this large sample coverage probability with the actual coverage probability of this confidence interval, obtained by simulation.     

A new test for high-dimensional regression coefficients in partially linear models

Partially linear regression models are semiparametric models that contain both linear and nonlinear components. They are extensively used in many scientific fields for their flexibility and convenient interpretability. In such analyses, testing the significance of the regression coefficients in the linear component is typically a key focus. Under the high-dimensional setting, i.e., “large p, small n,” the conventional F-test strategy does not apply because the coefficients need to be estimated through regularization techniques. In this article, we develop a new test using a U-statistic of order two, relying on a pseudo-estimate of the nonlinear component from the classical kernel method. Using the martingale central limit theorem, we prove the asymptotic normality of the proposed test statistic under some regularity conditions. We further demonstrate our proposed test's finite-sample performance by simulation studies and by analyzing some breast cancer gene expression data.