For statistical procedures including Scheffe-type simultaneous confidence bounds for response surfaces and likelihood ratio tests for an additional regressor with unspecified parameters in a regression model, the confidence level or size can be expressed in terms of probabilities of the form $P\lbrack U \in D(\Gamma, \theta)\rbrack$, where $\Gamma$ is a subset of $S^m$ (the unit sphere in $R^{m + 1}), U$ is uniformly distributed in $S^m$ and $D(\Gamma, \theta)$ denotes the…
We illustrate with contemporary examples Hotelling's geometric approach to simultaneous probability calculations. Hotelling reduces the evaluation of certain normal theory significance probabilities to finding the volume of a tube about a curve in a hypersphere, and shows that this volume is often exactly given by length times cross-sectional area. We review Hotelling's result together with some recent complements, and then use the approach to set simultaneous prediction…
In shrinkage estimation of a multivariate normal mean, the two dominant approaches to construction of estimators have been the hierarchical or empirical Bayes approach and the minimax approach. The first has been most extensively used in practice, because of its greater flexibility in adapting to varying situations, while the second has seen the most extensive theoretical development. In this paper we consider several topics on the interface of these approaches,…
Random left truncation is modelled by the conditional distribution of the random variable $X$ of interest, given that it is larger than the truncating random variable $Y$; usually $X$ and $Y$ are assumed independent. The present paper is based on a simple reparametrization of the left truncation model as a three-state Markov process. The derivation of a nonparametric estimator is a distribution function under random truncation is then a special case of results on the…
Consider a multiple linear regression in which $Y_i, i = 1, \cdots, n$, are independent normal variables with variance $\sigma^2$ and $E(Y_i) = \alpha + V'_i\beta$, where $V_i \in \mathbb{R}^r$ and $\beta \in \mathbb{R}^r.$ Let $\hat{\alpha}$ denote the usual least squares estimator of $\alpha$. Suppose that $V_i$ are themselves observations of independent multivariate normal random variables with mean 0 and known, nonsingular covariance matrix $\theta$. Then $\hat{\alpha}$ i…
Supposing a given collection $y_1, \cdots, y_N$ of i.i.d. random points on a Riemannian manifold, we discuss how to estimate the underlying distribution from a differential geometric viewpoint. The main hypothesis is that the manifold is closed and that the distribution is (sufficiently) smooth. Under such a hypothesis a convergence arbitrarily close to the $N^{-1/2}$ rate is possible, both in the $L_2$ and the $L_\infty$ senses.
Conditions for the existence of the density of a minimum contrast estimator in a parametric statistical family are given together with a formula for this density. The formula is exact if multiple local minima cannot occur; otherwise the formula is an exact expression for the point process of local minima of the contrast function. Although it is not in general feasible to compute the expression for the density, the formula can be used as a basis for further expansion of the…
In this paper we investigate the power function of the Studentised range test for comparing the means of normal populations in the one-way fixed effects analysis of variance model. The main results provide rigorous proofs of certain least favourable configurations of population means. These results are important in the calculation of the sample sizes required to guarantee power levels under certain restrictions on the ranges of the population means.
We show that the mathematical structure of belief functions makes them suitable for generating classes of prior distributions to be used in robust Bayesian inference. In particular, the upper and lower bounds of the posterior probability content of a measurable subset of the parameter space may be calculated directly in terms of upper and lower expectations (Theorem 4.1). We also extend an integral representation given by Dempster to infinite sets (Theorem 2.1).
We consider Bayes procedures for a location parameter $\theta$ that are robust with respect to the shape of the distribution $F$ of the data. The case where $F$ is fixed (nonrandom) and the case where $F$ has a Dirichlet distribution are both treated. The procedures are based on the posterior distributions of the location parameter given the partial information contained in a robust estimate of location. We show consistency and asymptotic normality of the procedures and give…
A smoothing spline estimator can be interpreted in two ways: either as the solution to a variational problem or as the posterior mean when a particular Gaussian prior is placed on the unknown regression function. In order to explain the remarkable performance of her Bayesian "confidence intervals" in a simulation study, Wahba conjectured that the average posterior variance of a spline estimate evaluated at the observation points will be close to the expected average squared…
Double censoring often occurs in collecting lifetime data and accordingly self-consistent estimators are widely employed for estimating the survival functions. In this paper we prove the weak convergence of self-consistent estimators using classical results on the Fredholm integral equation. Also, a method of calculating the asymptotic variance is presented.
Dose-response experiments are widely used in scientific research and nonmonotone dose-response curves are commonly observed. A class of nonparametric tests for dose-response curves subject to downturns at high doses is examined via its asymptotic properties. This class includes the well-known Jonckheere-Terpstra test as a limiting case. The analysis indicates that the Jonckheere-Terpstra test lacks robustness to a downturn in the dose-response, and that other members of the…
A class of estimates for regression parameters in a linear model with right censored data is proposed. These estimates are derived by using linear rank tests for right censored data as estimating equations. They are shown to be consistent and asymptotically normal with covariance matrix for which estimates are proposed. Efficient estimates within this class are derived together with conditions when they are fully efficient.
Techniques are developed for nonparametric analysis of data under a Cox-regression-like model permitting time-dependent covariate effects determined by a regression function $\beta_0(t)$. Estimators resulting from maximization of an appropriate penalized partial likelihood are shown to exist and a computational approach is outlined. Weak uniform consistency (with a rate of convergence) and pointwise asymptotic normality of the estimators are established under regularity…
We consider the semiparametric linear regression model with censored data and with unknown error distribution. We describe estimation equations of the Buckley-James type that admit $\sqrt n$-consistent and asymptotically normal solutions. The derived estimator is efficient at a particular error distribution. We show the equivalence between this type of estimator and an estimator based on a linear rank test suggested by Tsiatis. This equivalence is an extension of a basic…
Box and Leon, Shoemaker and Kackar have discussed the problem of closeness to target in quality engineering. If the mean response $f(x, z)$ depends on $(x, z)$, the variance function is a PERMIA if it is $g(z)$, i.e., depends only on $z$. The goal is to find $(x_0, z_0)$ which minimizes variance while achieving a target mean value. We pose and answer the question: For given smoothness assumptions about $f$ and $g$, how accurately can we estimate $x_0$ and $z_0$? As part of…
Several algorithms for image reconstruction in positron emission tomography (PET) have been described in the medical and statistical literature. We study a continuous idealization of the PET reconstruction problem, considered as an example of bivariate density estimation based on indirect observations. Given a large sample of indirect observations, we consider the size of the equivalent sample of observations, whose original exact positions would allow equally accurate…
Asymptotic properties of multivariate time series with characteristic roots on the unit circle are considered. For a vector autoregressive moving average (ARMA) process, we derive the limiting distributions of certain statistics which are useful in understanding nonstationary processes. These distributions are derived in a unified manner for all types of characteristic roots and are expressed in terms of stochastic integrals of Brownian motions. For applications, we use the…
We establish a new functional central limit theorem for empirical processes indexed by classes of functions. In a neighborhood of a fixed parameter point, an $n^{-1/3}$ rescaling of the parameter is compensated for by an $n^{2/3}$ rescaling of the empirical measure, resulting in a limiting Gaussian process. By means of a modified continuous mapping theorem for the location of the maximizing value, we deduce limit theorems for several statistics defined by maximization or…
We analyze the following model: One person, called "helper" observes an outcome $x^n = (x_1, \cdots, x_n) \in \mathscr{X}^n$ of the sequence $X^n = (X_1, \cdots, X_n)$ of i.i.d. RV's and the statistician gets a sample $y^n = (y_1, \cdots, y_n)$ of the sequence $Y^n(\theta, x^n)$ of RV's with a density $\prod^n_{t = 1} f(y_t \mid \theta, x_t)$. The helper can give some (side) information about $x^n$ to the statistician via an encoding function $s_n: \mathscr{X}^n \rightarrow \mathbb{N}$…
It is proved that, except for a location term, empirical likelihood does draw contours which are second-order correct for those of a pseudo-likelihood. However, except in the case of one dimension, this pseudo-likelihood is not that which would commonly be employed when constructing a likelihood-based confidence region. It is shown that empirical likelihood regions may be adjusted for location so as to render them second-order correct. Furthermore, it is proved that…
The comparison of nonparametric regression curves is considered. It is assumed that there are parametric (possibly nonlinear) transformations of the axes which map one curve into the other. Estimation and testing of the parameters in the transformations are studied. The rate of convergence is $n^{-1/2}$ although the nonparametric components of the model typically have a rate slower than that. A statistic is provided for testing the validity of a given completely parametric…
If $\{g_\theta(t)\}$ is a regular family of probability densities on the real line, with corresponding hazard rates $\{h_\theta(t)\}$, then the Fisher information for $\theta$ can be expressed in terms of the hazard rate as follows: $\mathscr{I}_\theta \equiv \int \big(\frac{\dot{g}_\theta}{g_\theta}\big)^2 g_\theta = \int \big(\frac{\dot{h}_\theta}{h_\theta}\big)^2 g_\theta, \theta \in \mathbb{R},$ where the dot denotes $\partial/\partial\theta$. This identity shows that…
For statistical procedures including Scheffe-type simultaneous confidence bounds for response surfaces and likelihood ratio tests for an additional regressor with unspecified parameters in a regression model, the confidence level or size can be expressed in terms of probabilities of the form P[U∈D(Γ,θ)], where Γ is a subset of Sm (the unit sphere in Rm+1),U is uniformly distributed in Sm and D(Γ,θ) denotes the…
Random left truncation is modelled by the conditional distribution of the random variable X of interest, given that it is larger than the truncating random variable Y; usually X and Y are assumed independent. The present paper is based on a simple reparametrization of the left truncation model as a three-state Markov process. The derivation of a nonparametric estimator is a distribution function under random truncation is then a special case of results on the…
Consider a multiple linear regression in which Yi,i=1,⋯,n, are independent normal variables with variance σ2 and E(Yi)=α+Vi′β, where Vi∈Rr and β∈Rr. Let α^ denote the usual least squares estimator of α. Suppose that Vi are themselves observations of independent multivariate normal random variables with mean 0 and known, nonsingular covariance matrix θ. Then α^ i…
Supposing a given collection y1,⋯,yN of i.i.d. random points on a Riemannian manifold, we discuss how to estimate the underlying distribution from a differential geometric viewpoint. The main hypothesis is that the manifold is closed and that the distribution is (sufficiently) smooth. Under such a hypothesis a convergence arbitrarily close to the N−1/2 rate is possible, both in the L2 and the L∞ senses.
We consider Bayes procedures for a location parameter θ that are robust with respect to the shape of the distribution F of the data. The case where F is fixed (nonrandom) and the case where F has a Dirichlet distribution are both treated. The procedures are based on the posterior distributions of the location parameter given the partial information contained in a robust estimate of location. We show consistency and asymptotic normality of the procedures and give…
Techniques are developed for nonparametric analysis of data under a Cox-regression-like model permitting time-dependent covariate effects determined by a regression function β0(t). Estimators resulting from maximization of an appropriate penalized partial likelihood are shown to exist and a computational approach is outlined. Weak uniform consistency (with a rate of convergence) and pointwise asymptotic normality of the estimators are established under regularity…
We consider the semiparametric linear regression model with censored data and with unknown error distribution. We describe estimation equations of the Buckley-James type that admit n-consistent and asymptotically normal solutions. The derived estimator is efficient at a particular error distribution. We show the equivalence between this type of estimator and an estimator based on a linear rank test suggested by Tsiatis. This equivalence is an extension of a basic…
Box and Leon, Shoemaker and Kackar have discussed the problem of closeness to target in quality engineering. If the mean response f(x,z) depends on (x,z), the variance function is a PERMIA if it is g(z), i.e., depends only on z. The goal is to find (x0,z0) which minimizes variance while achieving a target mean value. We pose and answer the question: For given smoothness assumptions about f and g, how accurately can we estimate x0 and z0? As part of…
We establish a new functional central limit theorem for empirical processes indexed by classes of functions. In a neighborhood of a fixed parameter point, an n−1/3 rescaling of the parameter is compensated for by an n2/3 rescaling of the empirical measure, resulting in a limiting Gaussian process. By means of a modified continuous mapping theorem for the location of the maximizing value, we deduce limit theorems for several statistics defined by maximization or…
We analyze the following model: One person, called "helper" observes an outcome xn=(x1,⋯,xn)∈Xn of the sequence Xn=(X1,⋯,Xn) of i.i.d. RV's and the statistician gets a sample yn=(y1,⋯,yn) of the sequence Yn(θ,xn) of RV's with a density ∏t=1nf(yt∣θ,xt). The helper can give some (side) information about xn to the statistician via an encoding function sn:Xn→N…
The comparison of nonparametric regression curves is considered. It is assumed that there are parametric (possibly nonlinear) transformations of the axes which map one curve into the other. Estimation and testing of the parameters in the transformations are studied. The rate of convergence is n−1/2 although the nonparametric components of the model typically have a rate slower than that. A statistic is provided for testing the validity of a given completely parametric…
If {gθ(t)} is a regular family of probability densities on the real line, with corresponding hazard rates {hθ(t)}, then the Fisher information for θ can be expressed in terms of the hazard rate as follows: Iθ≡∫(gθg˙θ)2gθ=∫(hθh˙θ)2gθ,θ∈R, where the dot denotes ∂/∂θ. This identity shows that…