Showing papers in "Journal of the American Statistical Association in 1999"

A Proportional Hazards Model for the Subdistribution of a Competing Risk

Abstract: With explanatory covariates, the standard analysis for competing risks data involves modeling the cause-specific hazard functions via a proportional hazards assumption Unfortunately, the cause-specific hazard function does not have a direct interpretation in terms of survival probabilities for the particular failure type In recent years many clinicians have begun using the cumulative incidence function, the marginal failure probabilities for a particular cause, which is intuitively appealing and more easily explained to the nonstatistician The cumulative incidence is especially relevant in cost-effectiveness analyses in which the survival probabilities are needed to determine treatment utility Previously, authors have considered methods for combining estimates of the cause-specific hazard functions under the proportional hazards formulation However, these methods do not allow the analyst to directly assess the effect of a covariate on the marginal probability function In this article we pro

Pooled Mean Group Estimation of Dynamic Heterogeneous Panels

Abstract: It is now quite common to have panels in which both T, the number of time series observations, and N, the number of groups, are quite large and of the same order of magnitude. The usual practice is either to estimate N separate regressions and calculate the coefficient means, which we call the mean group (MG) estimator, or to pool the data and assume that the slope coefficients and error variances are identical. In this article we propose an intermediate procedure, the pooled mean group (PMG) estimator, which constrains long-run coefficients to be identical but allows short-run coefficients and error variances to differ across groups. We consider both the case where the regressors are stationary and the case where they follow unit root processes, and for both cases derive the asymptotic distribution of the PMG estimators as T tends to infinity. We also provide two empirical applications: Aggregate consumption functions for 24 Organization for Economic Cooperation and Development economies over th...

Filtering via Simulation: Auxiliary Particle Filters

Abstract: This article analyses the recently suggested particle approach to filtering time series. We suggest that the algorithm is not robust to outliers for two reasons: the design of the simulators and the use of the discrete support to represent the sequentially updating prior distribution. Here we tackle the first of these problems.

Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs

Abstract: This article uses propensity score methods to estimate the treatment impact of the National Supported Work (NSW) Demonstration, a labor training program, on postintervention earnings. We use data from Lalonde's evaluation of nonexperimental methods that combine the treated units from a randomized evaluation of the NSW with nonexperimental comparison units drawn from survey datasets. We apply propensity score methods to this composite dataset and demonstrate that, relative to the estimators that Lalonde evaluates, propensity score estimates of the treatment impact are much closer to the experimental benchmark estimate. Propensity score methods assume that the variables associated with assignment to treatment are observed (referred to as ignorable treatment assignment, or selection on observables). Even under this assumption, it is difficult to control for differences between the treatment and comparison groups when they are dissimilar and when there are many preintervention variables. The estimate...

Introduction to matrix analytic methods in stochastic modeling

Abstract: Preface Part I. Quasi-Birth-and-Death Processes. 1. Examples Part II. The Method of Phases. 2. PH Distributions 3. Markovian Point Processes Part III. The Matrix-Geometric Distribution. 4. Birth-and-Death Processes 5. Processes Under a Taboo 6. Homogeneous QBDs 7. Stability Condition Part IV. Algorithms. 8. Algorithms for the Rate Matrix 9. Spectral Analysis 10. Finite QBDs 11. First Passage Times Part V. Beyond Simple QBDs. 12. Nonhomogeneous QBDs 13. Processes, Skip-Free in One Direction 14. Tree Processes 15. Product Form Networks 16. Nondenumerable States Bibliography Index.

Applied smoothing techniques for data analysis : the kernel approach with S-plus illustrations

Abstract: 1. Density estimation for exploring data 2. Density estimation for inference 3. Nonparametric regression for exploring data 4. Inference with nonparametric regression 5. Checking parametric regression models 6. Comparing regression curves and surfaces 7. Time series data 8. An introduction to semiparametric and additive models References

Goodness of Fit and Related Inference Processes for Quantile Regression

Abstract: We introduce a goodness-of-fit process for quantile regression analogous to the conventional R2 statistic of least squares regression. Several related inference processes designed to test composite hypotheses about the combined effect of several covariates over an entire range of conditional quantile functions are also formulated. The asymptotic behavior of the inference processes is shown to be closely related to earlier p-sample goodness-of-fit theory involving Bessel processes. The approach is illustrated with some hypothetical examples, an application to recent empirical models of international economic growth, and some Monte Carlo evidence.

Adjusting for Nonignorable Drop-Out Using Semiparametric Nonresponse Models

Abstract: Consider a study whose design calls for the study subjects to be followed from enrollment (time t = 0) to time t = T, at which point a primary endpoint of interest Y is to be measured. The design of the study also calls for measurements on a vector V t) of covariates to be made at one or more times t during the interval [0, T). We are interested in making inferences about the marginal mean μ0 of Y when some subjects drop out of the study at random times Q prior to the common fixed end of follow-up time T. The purpose of this article is to show how to make inferences about μ0 when the continuous drop-out time Q is modeled semiparametrically and no restrictions are placed on the joint distribution of the outcome and other measured variables. In particular, we consider two models for the conditional hazard of drop-out given (V(T), Y), where V(t) denotes the history of the process V t) through time t, t ∈ [0, T). In the first model, we assume that λQ(t|V(T), Y) exp(α0 Y), where α0 is a scalar paramet...

The Cambridge dictionary of statistics

Abstract: The Cambridge dictionary of statistics , The Cambridge dictionary of statistics , کتابخانه دیجیتال جندی شاپور اهواز

Introduction to Mathematical Systems Theory: A Behavioral Approach

Abstract: This is a book about modelling, analysis, and control of linear time-invariant systems. The book uses what is called the behavioral approach towards mathematical modelling. Thus a system is viewed as a dynamical relation between manifest and latent variables. The emphasis is on dynamical systems that are represented by systems of linear constant coefficient differential equations. In the first part of the book the structure of the set of trajectories that such dynamical systems generate is analyzed. Conditions are obtained for two systems of differential equations to be equivalent in the sense that they define the same behavior. It is further shown that the trajectories of such linear differential systems can be partitioned in free inputs and bound outputs. In addition the memory structure of the system is analyzed through state space models. The second part of the book is devoted to a number of important system properties, notably controllability, observability, and stability. An essential feature of using the behavioral approach is that it allows these and similar concepts to be introduced in a representation free manner. In the third part control problems are considered, more specifically stabilization and pole placement questions. The book is a textbook for advanced undergraduate or beginning graduate students in mathematics and engineering. It contains numerous exercises, including simulation problems, and examples, notably of mechanical systems and electrical circuits.

Classes of nonseparable, spatio-temporal stationary covariance functions

Abstract: Suppose that a random process Z(s;t), indexed in space and time, has spatio-temporal stationary covariance C(h;u), where h ∈ ℝd (d ≥ 1) is a spatial lag and u ∈ ℝ is a temporal lag. Separable spatio-temporal covariances have the property that they can be written as a product of a purely spatial covariance and a purely temporal covariance. Their ease of definition is counterbalanced by the rather limited class of random processes to which they correspond. In this article we derive a new approach that allows one to obtain many classes of nonseparable, spatio-temporal stationary covariance functions and fit several such classes to spatio-temporal data on wind speed over a region in the tropical western Pacific ocean.

A Primer on Wavelets and Their Scientific Applications

Abstract: Haar WaveletsThe Haar TransformConservation and Compaction of EnergyRemoving Noise from Audio SignalsHaar WaveletsMultiresolution AnalysisCompression of Audio SignalsRemoving Noise from Audio SignalsNotes and ReferencesDaubechies WaveletsThe Daub4 WaveletsConservation and Compaction of EnergyOther Daubechies WaveletsCompression of Audio SignalsQuantization, Entropy, and CompressionDenoising Audio SignalsTwo-Dimensional Wavelet TransformsCompression of ImagesFingerprint CompressionDenoising ImagesSome Topics in Image ProcessingNotes and ReferencesFrequency AnalysisDiscrete Fourier AnalysisCorrelation and Feature DetectionObject Detection in 2-D ImagesCreating Scaling Signals and WaveletsNotes and ReferencesBeyond WaveletsWavelet Packet TransformsApplications of Wavelet Packet TransformsContinuous Wavelet TransformsGabor Wavelets and Speech AnalysisNotes and ReferencesAppendix: Software for Wavelet Analysis

SiZer for Exploration of Structures in Curves

Abstract: In the use of smoothing methods in data analysis, an important question is which observed features are “really there,” as opposed to being spurious sampling artifacts. An approach is described based on scale-space ideas originally developed in the computer vision literature. Assessment of Significant ZERo crossings of derivatives results in the SiZer map, a graphical device for display of significance of features with respect to both location and scale. Here “scale” means “level of resolution”; that is, “bandwidth.”

A New Bayesian Model For Survival Data With a Surviving Fraction

Abstract: We consider Bayesian methods for right-censored survival data for populations with a surviving (cure) fraction. We propose a model that is quite different from the standard mixture model for cure rates. We provide a natural motivation and interpretation of the model and derive several novel properties of it. First, we show that the model has a proportional hazards structure, with the covariates depending naturally on the cure rate. Second, we derive several properties of the hazard function for the proposed model and establish mathematical relationships with the mixture model for cure rates. Prior elicitation is discussed in detail, and classes of noninformative and informative prior distributions are proposed. Several theoretical properties of the proposed priors and resulting posteriors are derived, and comparisons are made to the standard mixture model. A real dataset from a melanoma clinical trial is discussed in detail.

Modeling Uncertainty in Latent Class Membership: A Case Study in Criminology

Abstract: Social scientists are commonly interested in relating a latent trait (e.g., criminal tendency) to measurable individual covariates (e.g., poor parenting) to understand what defines or perhaps causes the latent trait. In this article we develop an efficient and convenient method for answering such questions. The basic model presumes that two types of variables have been measured: Response variables (possibly longitudinal) that partially determine the latent class membership, and covariates or risk factors that we wish to relate to these latent class variables. The model assumes that these observable variables are conditionally independent, given the latent class variable. We use a mixture model for the joint distribution of the observables. We apply this model to a longitudinal dataset assembled as part of the Cambridge Study of Delinquent Development to test a fundamental theory of criminal development. This theory holds that crime is committed by two distinct groups within the population: Adoles...

Parameter Expansion for Data Augmentation

Abstract: Viewing the observed data of a statistical model as incomplete and augmenting its missing parts are useful for clarifying concepts and central to the invention of two well-known statistical algorithms: expectation-maximization (EM) and data augmentation. Recently, Liu, Rubin, and Wu demonstrated that expanding the parameter space along with augmenting the missing data is useful for accelerating iterative computation in an EM algorithm. The main purpose of this article is to rigorously define a parameter expanded data augmentation (PX-DA) algorithm and to study its theoretical properties. The PX-DA is a special way of using auxiliary variables to accelerate Gibbs sampling algorithms and is closely related to reparameterization techniques. We obtain theoretical results concerning the convergence rate of the PX-DA algorithm and the choice of prior for the expansion parameter. To understand the role of the expansion parameter, we establish a new theory for iterative conditional sampling under the tra...

Methods for Estimating a Conditional Distribution Function

Abstract: Motivated by the problem of setting prediction intervals in time series analysis, we suggest two new methods for conditional distribution estimation. The first method is based on locally fitting a logistic model and is in the spirit of recent work on locally parametric techniques in density estimation. It produces distribution estimators that may be of arbitrarily high order but nevertheless always lie between 0 and 1. The second method involves an adjusted form of the Nadaraya–Watson estimator. It preserves the bias and variance properties of a class of second-order estimators introduced by Yu and Jones but has the added advantage of always being a distribution itself. Our methods also have application outside the time series setting; for example, to quantile estimation for independent data. This problem motivated the work of Yu and Jones.

Algorithms on Strings, Trees, and Sequences: Computer Science and Computational Biology

Abstract: Part I. Exact String Matching: The Fundamental String Problem: 1. Exact matching: fundamental preprocessing and first algorithms 2. Exact matching: classical comparison-based methods 3. Exact matching: a deeper look at classical methods 4. Semi-numerical string matching Part II. Suffix Trees and their Uses: 5. Introduction to suffix trees 6. Linear time construction of suffix trees 7. First applications of suffix trees 8. Constant time lowest common ancestor retrieval 9. More applications of suffix trees Part III. Inexact Matching, Sequence Alignment and Dynamic Programming: 10. The importance of (sub)sequence comparison in molecular biology 11. Core string edits, alignments and dynamic programming 12. Refining core string edits and alignments 13. Extending the core problems 14. Multiple string comparison: the Holy Grail 15. Sequence database and their uses: the motherlode Part IV. Currents, Cousins and Cameos: 16. Maps, mapping, sequencing and superstrings 17. Strings and evolutionary trees 18. Three short topics 19. Models of genome-level mutations.

Fast reliable algorithms for matrices with structure

Abstract: Contributors Preface Notation 1. Displacement structure and array algorithms Thomas Kailath 2. Stabilized Schur Algorithms Shivkumar Chandrasekaran and Ali H. Sayed 3. Fast Stable Solvers for Structured Linear Systems Ali H. Sayed and Shivkumar Chandrasekaran 4. Stability of Fast Algorithms for Structured Linear Systems Richard P. Brent 5. Iterative Methods for Linear Systems with Matrix Structure Raymond H. Chan and Michael K. Ng 6. Asymptotic Spectral Distribution of Toeplitz-Related Matrices Paolo Tilli 7. Newton's Iteration for Structured Matrices Victor Y. Pan, Sheryl Branham, Rhys E. Rosholt and Ai-Long Zheng 8. Fast Algorithms with Applications to Markov Chains and Queueing Models Dario A. Bini and Beatrice Meini 9. Tensor Displacement Structures and Polyspectral Matching Victor S. Grigorascu and Phillip A. Regalia 10. Minimal Complexity Realization of Structured Matrices Patrick Dewilde Appendix A. Useful Matrix Results Thomas Kailath and Ali H. Sayed Appendix B. Elementary Transformations Thomas Kailath and Ali H. Sayed Bibliography Index.

Identifiability, Improper Priors, and Gibbs Sampling for Generalized Linear Models

Abstract: Markov chain Monte Carlo algorithms are widely used in the fitting of generalized linear models (GLMs). Such model fitting is somewhat of an art form, requiring suitable trickery and tuning to obtain results in which one can have confidence. A wide range of practical issues arise. The focus here is on parameter identifiability and posterior propriety. In particular, we clarify that nonidentifiability arises for usual GLMs and discuss its implications for simulation-based model fitting. Because often some part of the prior specification is vague, we consider whether the resulting posterior is proper, providing rather general and easily checked results for GLMs. We also show that if a Gibbs sampler is run with an improper posterior, then it may be possible to use the output to obtain meaningful inference for certain model unknowns.

Estimation of the Causal Effect of a Time-Varying Exposure on the Marginal Mean of a Repeated Binary Outcome

Abstract: We provide sufficient conditions for estimating from longitudinal data the causal effect of a time-dependent exposure or treatment on the marginal probability of response for a dichotomous outcome. We then show how one can estimate this effect under these conditions using the g-computation algorithm of Robins. We also derive the conditions under which some current approaches to the analysis of longitudinal data, such as the generalized estimating equations (GEE) approach of Zeger and Liang, the feedback model techniques of Liang and Zeger, and within-subject conditional methods, can provide valid tests and estimates of causal effects. We use our methods to estimate the causal effect of maternal stress on the marginal probability of a child's illness from the Mothers' Stress and Children's Morbidity data and compare our results with those previously obtained by Zeger and Liang using a GEE approach.

Nonconjugate Bayesian Estimation of Covariance Matrices and its Use in Hierarchical Models

Abstract: The problem of estimating a covariance matrix in small samples has been considered by several authors following early work by Stein. This problem can be especially important in hierarchical models where the standard errors of fixed and random effects depend on estimation of the covariance matrix of the distribution of the random effects. We propose a set of hierarchical priors (HPs) for the covariance matrix that produce posterior shrinkage toward a specified structure—here we examine shrinkage toward diagonality. We then address the computational difficulties raised by incorporating these priors, and nonconjugate priors in general, into hierarchical models. We apply a combination of approximation, Gibbs sampling (possibly with a Metropolis step), and importance reweighting to fit the models, and compare this hybrid approach to alternative Markov Chain Monte Carlo methods. Our investigation involves three alternative HPs. The first works with the spectral decomposition of the covariance matrix an...

Hypothesis Testing in Time Series via the Empirical Characteristic Function: A Generalized Spectral Density Approach

Abstract: The standardized spectral density completely describes serial dependence of a Gaussian process. For non-Gaussian processes, however, it may become an inappropriate analytic tool, because it misses the nonlinear processes with zero autocorrelation. By generalizing the concept of the standardized spectral density, I propose a new spectral tool suitable for both linear and nonlinear time series analysis. The generalized spectral density is indexed by frequency and a pair of auxiliary parameters. It is well defined for both continuous and discrete random variables, and requires no moment condition. Introduction of the auxiliary parameters renders the spectrum able to capture all pairwise dependencies, including those with zero autocorrelation. The standardized spectral density can be derived by properly differentiating the generalized spectral density with respect to the auxiliary parameters at the origin. The consistency of a class of Parzen's kernel-type estimators for the generalized spectral dens...

On estimation of monotone and concave frontier functions

Abstract: When analyzing the productivity of firms, one may want to compare how the firms transform a set of inputs x (typically labor, energy or capital) into an output y (typically a quantity of goods produced). The economic efficiency of a firm is then defined in terms of its ability to operate close to or on the production frontier, the boundary of the production set. The frontier function gives the maximal level of output attainable by a firm for a given combination of its inputs. The efficiency of a firm may then be estimated via the distance between the attained production level and the optimal level given by the frontier function. From a statistical viewpoint, the frontier function may be viewed as the upper boundary of the support of the population of firms density in the input and output space. It is often reasonable to assume that the production frontier is a concave monotone function. Then a famous estimator in the univariate input and output case is the data envelopment analysis (DEA) estimato...

Regression depth. Commentaries. Rejoinder

Abstract: In this article we introduce a notion of depth in the regression setting. It provides the rank of any line (plane), rather than ranks of observations or residuals. In simple regression we can compute the depth of any line by a fast algorithm. For any bivariate dataset Z n of size n there exists a line with depth at least n/3. The largest depth in Z n can be used as a measure of linearity versus convexity. In both simple and multiple regression we introduce the deepest regression method, which generalizes the univariate median and is equivariant for monotone transformations of the response. Throughout, the errors may be skewed and heteroscedastic. We also consider depth-based regression quantiles. They estimate the quantiles of y given x, as do the Koenker-Bassett regression quantiles, but with the advantage of being robust to leverage outliers. We explore the analogies between depth in regression and in location, where Tukey's halfspace depth is a special case of our general definition. Also, Liu's simplicial depth can be extended to the regression framework.

Proportional hazards regression with missing covariates

Abstract: Nonparametric maximum likelihood (NPML) is used to estimate regression parameters in a proportional hazards regression model with missing covariates. The NPML estimator is shown to be consistent and asymptotically normally distributed under some conditions. EM type algorithms are applied to solve the maximization problem. Variance estimates of the regression parameters are obtained by a profile likelihood approach that uses EM-aided numerical differentiation. Simulation results indicate that the NPML estimates of the regression parameters are more efficient than the approximate partial likelihood estimates and estimates from complete-case analysis when missing covariates are missing completely at random, and that the proposed method corrects for bias when the missing covariates are missing at random.

A Dynamic Model of Purchase Timing with Application to Direct Marketing

Abstract: Predicting changes in individual customer behavior is an important element for success in any direct marketing activity. In this article we develop a hierarchical Bayes model of customer interpurchase times based on the generalized gamma distribution. The model allows for both cross-sectional and temporal heterogeneity, with the latter introduced through the component mixture model dependent on lagged covariates. The model is applied to personal investment data to predict when and if a specific customer will likely increase time between purchases. This prediction can be used managerially as a signal for the firm to use some type of intervention to keep that customer.