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This work presents a stochastic framework for the construction of rainfall intensity–time scale–return period relationships, which was applied in the recent regionalization of design rainfall curves over the Greek territory, described in a companion paper. The methodology outlined herein builds upon a widely-used mathematical framework, which has been recently revisited and upgraded, and incorporates two different versions: (a) a theoretically consistent stochastic model applicable for rainfall intensity over any scale of interest; and (b) a simplified version valid over small scales, which makes parameter estimation easier. Special attention is given to the presentation of the simplified version, which suffices for most engineering tasks. Parameter estimation approaches are presented in detail, including the K-moments framework that allows for reliable high-order moment estimation and handling of bias due to spatiotemporal dependence. [ABSTRACT FROM AUTHOR]

This article reviews Albert Einstein's first published paper, submitted for publication in 1900. At that time, Einstein was 21 and a recent college graduate. His paper uses modeling and least squares to analyze data in support of a scientific proposition. Einstein is shown to be well trained, for his day, in using statistics as a tool in his scientific research. This paper also shows his ability to make trivial arithmetic mistakes and some clumsiness in data recording. A major aim of this article is to help provide a better appreciation of Einstein as an active user of statistical arguments in this and other of his important publications. [ABSTRACT FROM AUTHOR]

Cross-trait polygenic risk score (PRS) method has gained popularity for assessing genetic correlation of complex traits using summary statistics from biobank-scale genome-wide association studies (GWAS). However, empirical evidence has shown a common bias phenomenon that highly significant cross-trait PRS can only account for a very small amount of genetic variance (R2 can be < 1 % ) in independent testing GWAS. The aim of this paper is to investigate and address the bias phenomenon of cross-trait PRS in numerous GWAS applications. We show that the estimated genetic correlation can be asymptotically biased toward zero. A consistent cross-trait PRS estimator is then proposed to correct such asymptotic bias. In addition, we investigate whether or not SNP screening by GWAS p-values can lead to improved estimation and show the effect of overlapping samples among GWAS. We analyze GWAS summary statistics of reaction time and brain structural magnetic resonance imaging-based features measured in the Pediatric Imaging, Neurocognition, and Genetics study. We find that the raw cross-trait PRS estimators heavily underestimate the genetic similarity between cognitive function and human brain structures (mean R 2 = 1.32 % ), whereas the bias-corrected estimators uncover the moderate degree of genetic overlap between these closely related heritable traits (mean R 2 = 22.42 % ). Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

One of the properties of such nonparametric classes is that they are broad enough, so misspecification of Graph HT ht stops being an issue. The present paper studies much more narrow choices of Graph HT ht . The latter also highlights what choices of Graph HT ht is most appropriate. [Extracted from the article]

Develops an approach to obtain nonparametric estimators for the number of classes in a finite population of known size. Approaches for dealing with difficulties caused by variations in class sizes; Correlation of different estimators; Investigation of estimator performance through an asymptomatic variance analysis and a Monte Carlo simulation study.

The paper presents a model for the evolution of an infectious disease in a population with individual-specific immunity. The immune state of an individual varies with time according to its own dynamics, depending on whether the individual is infected or not. The model involves a system of size-structured (first-order) PDEs that capture both the dynamics of the immune states and the transition between compartments consisting of infected, susceptible, etc. individuals. Due to the unavailability of precise data about the immune states of the individuals, the main focus in the paper is on developing a technique for set-membership estimations of aggregated quantities of interest. The technique involves solving specific optimization problems for the underlying PDE system and is developed up to a numerical method. Results of numerical simulations are presented for a benchmark model of SIS-type, potentially applicable to diseases like influenza and to various sexually transmitted diseases. [ABSTRACT FROM AUTHOR]

Estimation of hydraulic properties in the field is usually small-scale and not cost-effective. This paper proposes an innovative method for estimating hydraulic diffusivity at regional scale. Monthly groundwater storage change over the period from 2003 to 2013 is first estimated from GRACE-derived terrestrial water storage (TWS). Assuming that the aquifer system is unconfined and the hydraulic properties are uniform in a geographical cell, the water balance principle and Darcy’s law are used to establish a relation between groundwater storage and hydraulic diffusivity. The value of hydraulic diffusivity is then adjusted using the generalized least squares and linear correlation method. This GRACE-derived hydraulic diffusivity estimation method, or GHDE method for short, is first verified with a hypothetical case and then applied in the Beishan area with available field-measured hydraulic conductivity data. The hypothetical case study demonstrates that the method works perfectly if the TWS data are error free. The Beishan case study illustrates that the estimated hydraulic diffusivities using the GHDE method correlate reasonably well with field test results, suggesting that this method is applicable. The accuracy of this method is constrained by the resolution of the GRACE-derived TWS data and is most suitable for very large scale groundwater problems due to the current accuracy of the GRACE data.EDITOR A. Castellarin ASSOCIATE EDITOR N. Verhoest [ABSTRACT FROM PUBLISHER]

In simple random sampling setting, the ratio estimator is more efficient than the mean of a simple random sampling without replacement (SRSWOR) if ρyx > 1/2 Cx/Cy, provided R > 0, which is usually the case. This shows that if auxiliary information is such that ρyx < -1/2 Cx/Cy, then we cannot use the ratio method of estimation to improve the sample mean as an estimator of population mean. So there is need for another type of estimator which also makes use of information on auxiliary variable x. Product method of estimation is an attempt in this direction. Product-type estimators are widely used for estimating population mean when the correlation between study and auxiliary variables is negatively high. This paper is developed to the study of the estimation of the population mean using of unbiased dual to product estimator by incorporating robust modified maximum likelihood estimators (MMLE's). Their properties have been obtained theoretically. For the support of the theoretical results, simulations studies under several super-population models have been made. We study the robustness properties of the modified estimators. We show that the utilization of MMLE's in estimating finite population mean results to robust estimates, which is very gainful when we have non-normality or common data anomalies such as outliers. [ABSTRACT FROM AUTHOR]

Composite steel-concrete beams are commonly used as flooring in buildings. The composite action between slab and steel joist is typically provided by shear connectors welded to the top of the steel joist and embedded in the concrete. This paper investigates the effects of material uncertainties on the numerically simulated structural response of simply-supported beam tests reported in the literature by means of Monte Carlo simulation (MCS). The numerical analyses are performed using a three-dimensional finite element model developed using the commercial software ABAQUS and capable of predicting the response of composite steel-concrete members as well as the influence of the shear connectors without having to rely on shear connection load-slip curves obtained from push-out tests. All materials are assumed to behave in a non-linear fashion. Contact regions between the concrete and steel elements are simulated using surface-to-surface and embedment techniques. The statistical information on the structural response obtained from MCS using different realisation sizes is compared and discussed. For the particular case studies considered in this paper it can be concluded that even a reduced number of realisations can already provide meaningful statistical representations of the structural response of the considered composite floor beams [ABSTRACT FROM AUTHOR]

This paper presents a novel data-driven bandwidth selector compatible with the small bandwidth asymptotics developed in Cattaneo, Crump, and Jansson (2009) for density-weighted average derivatives. The new bandwidth selector is of the plug-in variety, and is obtained based on a mean squared error expansion of the estimator of interest. An extensive Monte Carlo experiment shows a remarkable improvement in performance when the bandwidth-dependent robust inference procedures proposed by Cattaneo, Crump, and Jansson (2009) are coupled with this new data-driven bandwidth selector. The resulting robust data-driven confidence intervals compare favorably to the alternative procedures available in the literature. The online supplemental material to this paper contains further results from the simulation study. [ABSTRACT FROM AUTHOR]

Missing data is a ubiquitous problem in medical and social sciences. It is well known that inferences based only on the complete data may not only lose efficiency, but may also lead to biased results if the data is not missing completely at random (MCAR). The inverse-probability weighting method proposed by Horvitz and Thompson (1952) is a popular alternative when the data is not MCAR. The Horvitz-Thompson method, however, is sensitive to the inverse weights and may suffer from loss of efficiency. In this paper, we propose a unified empirical likelihood approach to missing data problems and explore the use of empirical likelihood to effectively combine unbiased estimating equations when the number of estimating equations is greater than the number of unknown parameters. One important feature of this approach is the separation of the complete data unbiased estimating equations from the incomplete data unbiased estimating equations. The proposed method can achieve semiparametric efficiency if the probability of missingness is correctly specified. Simulation results show that the proposed method has better finite sample performance than its competitors. Supplemental materials for this paper, including proofs of the main theoretical results and the R code used for the NHANES example, are available online on the journal website. [ABSTRACT FROM AUTHOR]

This paper contributes to the debate over the effects of religious pluralism on religosity. A key innovative feature of the paper is the use of voluntary contributions from members as a measure of religiosity. Using data from 177 congregations, and employing both the Herfindahl Index and market share as measures of religious market competition, a two-stage least squares estimation technique shows that Protestants give more when their congregation is faced with substantial competition, while Catholics tend to contribute more when they represent a minority church in a heavily concentrated market. This outcome is explained by arguing that churches with more competition react by providing a more satisfactory product and do a better job of filling niches in the religious marketplace. [ABSTRACT FROM AUTHOR]

From the theoretical standpoint, this paper attempts to integrate the objective, cognitive and normative dimensions of social mobility. This integration is based on a comparison of the matrices of actual, perceived and equitable frequencies of transitions between origins and destinations. The ratios of compared frequencies operationally define the degree of false consciousness, feeling of injustice and illegitimacy in the domain of social mobility. From the methodological standpoint we propose a practical method for ascertaining cognitive and normative mobility frequencies. In this paper we used average frequencies for the entire sample, although an analysis of individual images and preferences of mobility frequencies can be performed. This extension would seem particularly useful for heterogeneous samples with respect to the social positions of the respondents. From the empirical standpoint we have demonstrated that, net of marginal effects, the matrices of actual, perceived and equitable mobility do not differ very much from each other, at least in terms of the goodness of fit. Within a small range of differences the measure of false consciousness has a smaller value than the measure of illegitimacy, which is still smaller than the measure of the feeling of injustice. [ABSTRACT FROM AUTHOR]

In this paper we are concerned with inferences from a sample survey to a finite population. We contrast inferences that are dependent on an assumed model with inferences based on the randomization induced by the sample selection plan. Randomization consistency for finite population estimators is defined and adopted as a requirement of probability sampling. A numerical example is examined to illustrate the dangers in the use of model-dependent estimators even when the model is apparently consonant with the sample data. The paper concludes with a summary of principles that we believe should guide the practitioner of sample surveys of finite populations. [ABSTRACT FROM AUTHOR]

In this article, the author comments on the research paper of John J. Wiorkowski titled "Estimating Volumes of Remaining Fossil Fuel Resources: A Critical Review," published in the September 1981 issue of "Journal of the American Statistical Association." According to the author, the strength of Wiorkowski's paper is in bringing the important problem of petroleum resource estimation to the attention of statisticians. The major weaknesses are in the organization and classification of models and the gaps in coverage. The major issues in resource estimation that Wiorkowski discusses are models for estimating the total oil and gas remaining to be found and a closely related problem, estimating the amount of appreciation in known fields. The problem with the discussion of the models is the lack of a proper classification scheme. Although Wiorkowski mentions the model of researchers E. Barouch and G.M. Kaufman, he fails to note that it is one of a general class of models that are based on the discovery process and fails to emphasize that it is a play model, whereas the others he discusses are used at a higher level of aggregation.

This paper unites two different fields, survival and contingency table analysis, in a single analytical framework based on the log-linear model. We demonstrate that many currently popular approaches to modeling survival data, including the approaches of Glasser (1967), Cox (1972), Breslow (1972, 1974), and Holford (1976), can be handled by using existing computer packages developed for the log-linear analysis of contingency table data. More important, we demonstrate that the log-linear modeling system used to characterize counted data structures directly characterizes survival data as well. Counted data methodologies for testing and estimation are also applicable here. Much of the theoretical basis for this work has been independently derived by Holford (1980) and Aitkin and Clayton (1980). The emphasis in this paper is not to develop new methodologies, but rather to present new uses and interpretations for already familiar methodologies. [ABSTRACT FROM AUTHOR]

The author is pleased to discuss a paper on the estimation of probability density functions and the location of bumps. There is an extensive literature on density estimation, but many statisticians seem doubtful about the usefulness of these techniques because their application seems subjective and complicated. A major criticism the author would make of this paper is that it does not help to dispel this negative attitude of statisticians toward density estimation. One cannot help but be impressed by the ingenuity of I. J. Good and R. A. Gaskins and even to believe that they may be able to successfully fit probability densities to data. The existence of bumps at the extremes of a sample can be investigated for large sample sizes by treating the bottom and top ends of the original sample as two new samples to be analyzed by themselves. Statistical scientists should heed the flippant advice of Sir Arthur Eddington: "Never trust an experimental result until it has been confirmed by theory."

Home value plays an important role in a variety of fields of research. In this paper, three commonly used measures of home value are evaluated and compared using a framework developed by Joreskog and Goldberger (1975) for estimation of causal models containing unobserved variables. The paper extends ideas presented by Kain and Quigley (1972) and Kish and Lansing (1954). [ABSTRACT FROM AUTHOR]

This paper is concerned with bias-reduction properties of that generalized jackknife which pertains to a single-estimator, single sample situation. Jackknifing is a technique for reducing bias by exploiting the dependence of the bias on sample size. In practice, this is carried out by reestimating the unknown parameter(s) with only part of the sample and combining the new and original estimates suitably weighted to produce cancellation in their biases. The size of the subsample affects both the bias and variance of the jackknife. While few results are available, "minimal data omission" seems sensible on grounds of variance. This paper shows the same is always true on grounds of bias. [ABSTRACT FROM AUTHOR]

A major concern in large-scale surveys is the problem of subpopulation estimation (domain estimation). This paper presents a study of four estimators for estimating domain totals. The domain considered in the study is an area type of domain, that is, a domain consisting of a combination of a certain number of area units belonging to different strata This paper uses some actual data and some fictitious data to compare variances and mean square errors of the four estimators. [ABSTRACT FROM AUTHOR]

Trip travel time reliability is an important measure of transportation system performance and a key factor affecting travelers' choices. This paper explores a method for estimating travel time distributions for corridors that contain multiple bottlenecks. A set of analytical equations are used to calculate the number of queued vehicles ahead of a probe vehicle and further capture many important factors affecting travel times: the prevailing congestion level, queue discharge rates at the bottlenecks, and flow rates associated with merges and diverges. Based on multiple random scenarios and a vector of arrival times, the lane-by-lane delay at each bottleneck along the corridor is recursively estimated to produce a route-level travel time distribution. The model incorporates stochastic variations of bottleneck capacity and demand and explains the travel time correlations between sequential links. Its data needs are the entering and exiting flow rates and a sense of the lane-by-lane distribution of traffic at each bottleneck. A detailed vehicle trajectory data-set from the Next Generation SIMulation (NGSIM) project has been used to verify that the estimated distributions are valid, and the sources of estimation error are examined. [ABSTRACT FROM AUTHOR]

The collocation technique has become a popular tool in oceanography and hydrology for estimating the error variances of different data sources such asin situsensors, models and remote sensing products. It is also possible to determine calibration constants, for example to account for an off-set between the data sources. So far, the temporal autocorrelation structure of the errors has not been studied, although it is known that it has detrimental effects on the results of the collocation technique, in particular when calibration constants are also determined. This paper shows how the (triple) collocation estimators can be adapted to retrieve the autocovariance functions; the statistical properties as well as the structural deficencies are described. The coupling between the autocorrelation of the error and the estimation of calibration constants is studied in detail, due to its importance for analysing temporal changes. In soil moisture applications, such time variations can be induced, for example, by seasonal changes in the vegetation cover, which affect both models and remote sensing products. The limitations of the proposed technique associated with these considerations are analysed using remote sensing andin situsoil moisture data. The variability of the inter-sensor calibration and the autocovariance are shown to be closely related to temporal patterns of the data. EditorD. Koutsoyiannis CitationZwieback, S., Dorigo, W., and Wagner, W., 2013. Estimation of the temporal autocorrelation structure by the collocation technique with an emphasis on soil moisture studies.Hydrological Sciences Journal, 58 (8), 1729–1747. [ABSTRACT FROM PUBLISHER]

Presents the authors' comments on the paper "Semilinear High-Dimensional Model for Normalization of Microarray Data: A Theoretical Analysis and Partial Consistency," by Jianqing Fan, Heng Peng, and Tao Huang, published in the September 2005 issue of the "Journal of the American Statistical Association." Congratulations to the authors for their interesting, innovative, and important contribution on microarray normalization; Analysis of the main contributions made by the authors; Suggestions for some possible extensions to the results of the authors; Views on the goal of significance analysis; Appreciation of the paper for its computational simplicity.

Presents the author's comments on the paper "Semilinear High-Dimensional Model for Normalization of Microarray Data: A Theoretical Analysis and Partial Consistency," by Jianqing Fan, Heng Peng and Tao Huang, published in the September 2005 issue of the "Journal of the American Statistical Association." Appreciation of the authors for their significant contribution in enlarging the bag of statistical tools available for normalization; Opinion on the normalization strategy described; Importance of the iterative procedure suggested; Views on the focus of the paper from a methodological standpoint; Contexts in which the methodology described may be applicable.

Rock fragment content and grain size affect the structure of soil and movement properties of soil water. In this paper, influences of rock fragment content and grain size on infiltration characteristics and hydraulic parameters of stony-soil media were analysed on the basis of one-dimensional horizontal absorption experiments. Two methods for estimating diffusivity of stony soils, using cumulative infiltration, wetting front and soil water content distribution, respectively, were compared to evaluate their applicability. Also, estimating method of Brooks–Corey water retention curve for stony soils, using cumulative infiltration and wetting front, was evaluated based on the measured retention curve. The results showed that both rock fragment content and grain size had significant effects on infiltration properties and hydraulic parameters of stony-soil media. Both cumulative infiltration and wetting front distance of stony-soil media were smaller than those of fine soil since rock fragment hindered infiltration. The more rock fragment content, the slower soil water moved, but the turning point appeared at the rock fragment content of 40%. Besides, larger rock fragment grain size accelerated infiltration at certain content. Both rock fragment content and grain size had significant influences on hydraulic parameters, and there were linear relationships between parameters and rock fragment content. Two kinds of methods for estimating diffusivity of stony soils had a very good consistency for some experimental scenarios. Besides, cumulative infiltration and wetting front could be used to accurately estimate water retention curve of stony soils. [ABSTRACT FROM PUBLISHER]

Event-based methods are used in flood estimation to obtain the entire flood hydrograph. Previously, such methods adopted in the UK have relied on pre-determined values of the input variables (e.g. rainfall and antecedent conditions) to a rainfall–runoff model, which is expected to result in an output flood of a particular return period. In contrast, this paper presents a method that allows all the input variables to take on values across the full range of their individual distributions. These values are then brought together in all possible combinations as input to an event-based rainfall–runoff model in a Monte Carlo simulation approach. Further, this simulation strategy produces a long string of events (on average 10 per year), where dependencies from one event to the next, as well as between different variables within a single event, are accounted for. Frequency analysis is then applied to the annual maximum peak flows and flow volumes. [ABSTRACT FROM AUTHOR]

This paper presents an innovative prognostic model based on health state probability estimation embedded in the closed loop diagnostic and prognostic system to provide comprehensive timely analysis for effective decision making in industrial asset management. To apply an appropriate classifier in health state probability estimation for prediction of machine remnant life, a comparative study on intelligent fault classification for four fault conditions with five different fault progressed data from high pressure cryogenic pumps were conducted. Five different classifiers, such as support vector machines (SVMs), radial basis function neural networks, random forest and linear regression, were used to evaluate their effectiveness for the health state estimation process. Two sets of impeller-rub data were employed for the prediction of pump remnant life based on estimation of discrete health state probability with feature selection technique using the outstanding capability of SVM. The results obtained were very encouraging and showed that the proposed prognosis system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications. [ABSTRACT FROM AUTHOR]

This paper presents an innovative prognostic model based on health state probability estimation embedded in the closed loop diagnostic and prognostic system to provide comprehensive timely analysis for effective decision making in industrial asset management. To apply an appropriate classifier in health state probability estimation for prediction of machine remnant life, a comparative study on intelligent fault classification for four fault conditions with five different fault progressed data from high pressure cryogenic pumps were conducted. Five different classifiers, such as support vector machines (SVMs), radial basis function neural networks, random forest and linear regression, were used to evaluate their effectiveness for the health state estimation process. Two sets of impeller-rub data were employed for the prediction of pump remnant life based on estimation of discrete health state probability with feature selection technique using the outstanding capability of SVM. The results obtained were very encouraging and showed that the proposed prognosis system has the potential to be used as an estimation tool for machine remnant life prediction in real life industrial applications. [ABSTRACT FROM AUTHOR]

In this paper we offer a complete methodology of cumulative slicing estimation to sufficient dimension reduction. In parallel to the classical slicing estimation, we develop three methods that are termed, respectively, as cumulative mean estimation, cumulative variance estimation, and cumulative directional regression. The strong consistency for p= O( n1 / 2 / log n) and the asymptotic normality for p= o( n1 / 2) are established, where p is the dimension of the predictors and n is sample size. Such asymptotic results improve the rate p= o( n1 / 3) in many existing contexts of semiparametric modeling. In addition, we propose a modified BIC-type criterion to estimate the structural dimension of the central subspace. Its consistency is established when p= o( n1 / 2). Extensive simulations are carried out for comparison with existing methods and a real data example is presented for illustration. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology for combining satellite images with advection-diffusion models for interpolation and prediction of environmental processes. We propose a dynamic state-space model and an ensemble Kalman filter and smoothing algorithm for on-line and retrospective state estimation. Our approach addresses the high dimensionality, measurement bias, and nonlinearities inherent in satellite data. We apply the method to a sequence of SeaWiFS satellite images in Lake Michigan from March 1998, when a large sediment plume was observed in the images following a major storm event. Using our approach, we combine the images with a sediment transport model to produce maps of sediment concentrations and uncertainties over space and time. We show that our approach improves out-of-sample RMSE by 20%-30% relative to standard approaches. This article has supplementary material online. [ABSTRACT FROM AUTHOR]

The functional mobility of the elderly is a very important factor in aging research, and prognostic information is valuable in making clinical and health care policy decisions. We develop a predictive model for the functional status of the elderly based on data from the Second Longitudinal Study of Aging (LSOA II). The functional status is an ordinal response variable. The ordered probit model has been moderately successful in analyzing such data; however, its reliance on the normal distribution for its latent variable hinders its accuracy and potential. In this paper, we focus on the prediction of conditional quantiles of the functional status based on a more general transformation model. The proposed estimation procedure does not rely on any parametric specification of the conditional distribution functions, aiming to reduce model misspecification errors in the prediction. Cross-validation within the LSOA II data shows that our prediction intervals are more informative than those from the ordered probit model. Monte Carlo simulations also demonstrate the merits of our approach in the analysis of ordinal response variables. [ABSTRACT FROM AUTHOR]

This paper studies the maximum likelihood estimation of a class of inverse Gaussian process models for degradation data. Both the subject-to-subject heterogeneity and covariate information can be incorporated into the model in a natural way. The EM algorithm is used to obtain the maximum likelihood estimators of the unknown parameters and the bootstrap is used to assess the variability of the maximum likelihood estimators. Simulations are used to validate the method. The model is fitted to laser data and corresponding goodness-of-fit tests are carried out. Failure time distributions in terms of degradation level passages are calculated and illustrated. The supplemental materials for this article are available online. [ABSTRACT FROM AUTHOR]

Quantile regression has emerged as a significant extension of traditional linear models and its potential in survival applications has recently been recognized. In this paper we study quantile regression with competing risks data, formulating the model based on conditional quantiles defined using the cumulative incidence function, which includes as a special case an analog to the usual accelerated failure time model. The proposed competing risks quantile regression model provides meaningful physical interpretations of covariate effects and, moreover, relaxes the constancy constraint on regression coefficients, thereby providing a useful, perhaps more flexible, alternative to the popular subdistribution proportional hazards model. We derive an unbiased monotone estimating equation for regression parameters in the quantile model. The uniform consistency and weak convergence of the resulting estimators are established across a quantile continuum. We develop inferences, including covariance estimation, second-stage exploration, and model diagnostics, which can be stably implemented using standard statistical software without involving smoothing or resampling. Our proposals are illustrated via simulation studies and an application to a breast cancer clinical trial. [ABSTRACT FROM AUTHOR]

Regression quantiles can be substantially biased when the covariates are measured with error. In this paper we propose a new method that produces consistent linear quantile estimation in the presence of covariate measurement error. The method corrects the measurement error induced bias by constructing joint estimating equations that simultaneously hold for all the quantile levels. An iterative EM-type estimation algorithm to obtain the solutions to such joint estimation equations is provided. The finite sample performance of the proposed method is investigated in a simulation study, and compared to the standard regression calibration approach. Finally, we apply our methodology to part of the National Collaborative Perinatal Project growth data, a longitudinal study with an unusual measurement error structure. [ABSTRACT FROM AUTHOR]

Wheat is the main grain crop in China. The planted area has increased to more than 300 million mu (about 20 million ha) and the rate of machinery harvest has reached about 80%. It is difficult to provide accurate information of wheat yield per mu in different areas. This makes national macro-management and safeguarding food security difficult. The combine harvester onboard information platform studied in this paper has two functions. First, it detects geographical information, harvested area and crop yield per mu and, second it reports the state of the harvester and diagnoses faults. The information platform provides service information for the driver which improves reliability. In addition, it can send this information to an information centre through a GSM (mobile phone) message module, and this centre can forward the information to government administration and research institutes through the network. The on board information platform cored with SCM 80C196 confirms geographical information of the harvester by GPS (global positioning system) to create an electronic map of the harvest area with actual harvest width and distance of travel. With measurements taken when unloading the grain, crop yield per mu can be calculated and the running status of the harvester as well as fault diagnosis through monitoring sensors can be appraised. Experiment shows that the measurement accuracy of harvest area and thus of yield per mu is about 95%. [ABSTRACT FROM AUTHOR]

Presents the authors' comments on the paper "Semilinear High-Dimensional Model for Normalization of Microarray Data: A Theoretical Analysis and Partial Consistency," by Jianqing Fan, Heng Peng and Tao Huang, published in the September 2005 issue of the "Journal of the American Statistical Association." Appreciation of the authors for their significant contribution to the semiparametric estimation theory; Suggestions for a two-way semilinear model for normalization and analysis of cDNA microarray data; Comments on multiway semilinear models; Description of models for location normalization; Opinion on the relationship between the relationship between the multiway semilinear models and the semilinear in-slide model.

Presents the authors' views in response to comments on their paper "Semilinear High-Dimensional Model for Normalization of Microarray Data: A Theoretical Analysis and Partial Consistency," published in the September 2005 issue of the "Journal of the American Statistical Association." Acknowledgement of the critics for their comments; Account of progress made in the field of statistical techniques for the design and analysis of microarray data; Explanations for the suggestions made by the critics for inclusion of more statistical understanding of various procedures.

Comments on a paper which developed a model for clustered functional data through a hierarchical nonparametric mixed-effects structure. Types of data that constitute clustered functional data; Features of the general nonparametric approach; Estimation methods.

Presents a response to commentaries on a paper which developed a methodology for function estimation by embedding numerous related nonparametric wavelet regressions within a hierarchical nested random-effects framework. Computation of Bayes factors for statistical inference on the treatment functions; Amount of shrinkage toward 0; Challenge in modeling the covariance of the basis functions.

In this paper, we introduce nonlinear regularized wavelet estimators for estimating nonparametric regression functions when sampling points are not uniformly spaced. The approach can apply readily to many other statistical contexts. Various new penalty functions are proposed. The hard-thresholding and soft-thresholding estimators of Donoho and Johnstone are specific members of nonlinear regularized wavelet estimators. They correspond to the lower and upper envelopes of a class of the penalized least squares estimators. Necessary conditions for penalty functions are given for regularized estimators to possess thresholding properties. Oracle inequalities and universal thresholding parameters are obtained for a large class of penalty functions. The sampling properties of nonlinear regularized wavelet estimators are established and are shown to be adaptively minimax. To efficiently solve penalized least squares problems, nonlinear regularized Sobolev interpolators (NRSI) are proposed as initial estimators, which are shown to have good sampling properties. The NRSI is further ameliorated by regularized one-step estimators, which are the one-step estimators of the penalized least squares problems using the NRSI as initial estimators. The graduated nonconvexity algorithm is also introduced to handle penalized least squares problems. They newly introduced approaches are illustrated by a few numerical examples. [ABSTRACT FROM AUTHOR]

This article discusses the state of statistical process control as we proceed into the 21st century. SPC refers to some statistical methods used extensively to monitor and improve the quality and productivity of manufacturing processes and service operations. SPC primarily involves the implementation of control charts, which are used to detect any change in a process that may affect the quality of the output. The first control charts were developed by Walter A. Shewhart in the 1920s. These simple Shewhart charts have dominated applications to date. The process-monitoring problem can be described in general terms. Murphy's law explains the purpose monitoring: over time, something will inevitably change and possibly cause deterioration in process quality. The Shewhart charts were designed to make it relatively easy for process personnel without statistical training to set up, apply, and interpret the charts using only a pencil and paper for calculations. Bayesian procedures appear to be naturally suited for process monitoring. The standard approach to sampling for a control chart is to use a fixed sampling rate in which samples of fixed size are obtained using a fixed-length sampling interval.

In this article the author comments on the research paper entitled "The Design and Analysis of the Observational Study--A Review," by Sonja M. McKinlay. The author remarks that although MeKinlay devotes a large part of her review to the developments in the analysis and interpretation of contingency tables, she refers the readers to papers by other statisticians too, for a historical perspective. Since the loglinear model approach now dominates the statistical literature on contingency table analysis, one often seem to ignore the ideas of statistician K. Pearson, which according to the author is a mistake. While the multivariate generalizations of the statisticians cross-product ratio or loglinear model approach were fermenting, the technique of standardization to eliminate the effects of categorical covariates received considerable attention in the epidemiological literature. The major advances in the literature on multidimensional contingency tables in the 1960's coincided with the emergence of interest in and the availability of high-speed computers, and this work received substantial impetus from several large-scale data analysis projects.

In regression analysis, the response is often transformed to remove heteroscedasticity and/or skewness. When a model already exists for the untransformed response, then it can be preserved by applying the same transform to both the model and the response. This methodology, which we call "transform both sides," has been applied in several recent papers and appears highly useful in practice. When a parametric transformation family such as the power transformations is used, then the transformation can be estimated by maximum likelihood. The maximum likelihood estimator, however, is very sensitive to outliers. In this article, we propose diagnostics to indicate cases influential for the transformation or regression parameters. [ABSTRACT FROM AUTHOR]

Although the problem of missing data arises in most branches of the discipline, it has received little systematic treatment in the geographical literature. In an effort to overcome this deficiency, this paper reviews a number of methods for approaching the problem. Of the three classes of solutions-ad hoc, cartographic interpolation, and statistical-the statistical approaches appear to be preferable. In this review a modified version of the Orchard end Woodbury missing-information principle receives the greatest emphasis because it combines classical statistical theory with trend surface and spatial autoregressive models. Although the best solution of ell is to return to the experimental situation in order to collect supplementary data, this is often impractical or impossible. The analyst should then consider the estimation techniques presented here. The methods used to address the missing data problem thus be- come an important stage in the overall process of experimental design, sampling, and hypothesis testing. [ABSTRACT FROM AUTHOR]

Frequently, dynamic analyses of public opinion cannot be performed because of incomplete polling. This problem is clearly evident for Acquired immune Deficiency Syndrome (AIDS); Thus to Overcome this limitation, the present paper demonstrates the usefulness of a new methodology whereby opinion (e.g., the spread of AIDS) can be estimated using the mathematical model of ideodynamics. As input, the model uses information in the mass media scored by computer, using the technique of successive filtrations. Such opinion computations can be made over any period of time, however long or short, so long as the mass media provide the major information about the issue. [ABSTRACT FROM AUTHOR]

While health, itself, is the central concept in the sociology of health, little agreement exists on what constitutes an appropriate definition of health. This paper develops two composite health status measures (one continuous summary measure, and one set of eight discrete health state measures) based on the World Health Organization's conceptualization of the physical, social, and psychological dimensions of health. Data from a 1978 regional survey demonstrates the continuous summary measure's reliability (alpha = .70), and validity (factor analytic support for the hypothesized dimensions). The results also indicate that although either of the comprehensive measures significantly increase the goodness of fit of structural models of health service utilization, the set of discrete health state measures increases more the goodness of fit, bringing the explained variance of physician visits up from 16 percent to 30 percent. The magnitude of the increments clearly demonstrates the considerable utility of the comprehensive health status measurement approach. [ABSTRACT FROM AUTHOR]

We draw attention to two problems associated with the use of instrumental variables (IV), the importance of which for empirical work has not been fully appreciated. First, the use of instruments that explain little of the variation in the endogenous explanatory variables can lead to large inconsistencies in the IV estimates even if only a weak relationship exists between the instruments and the error in the structural equation. Second, in finite samples, IV estimates are biased in the same direction as ordinary least squares (OLS) estimates. The magnitude of the bias of IV estimates approaches that of OLS estimates as the R² between the instruments and the endogenous explanatory variable approaches 0. To illustrate these problems, we reexamine the results of a recent paper by Angrist and Krueger, who used large samples from the U.S. Census to estimate wage equations in which quarter of birth is used as an instrument for educational attainment. We find evidence that, despite huge sample sizes, their IV estimates may suffer from finite-sample bias and may be inconsistent as well. These findings suggest that valid instruments may be more difficult to find than previously imagined. They also indicate that the use of large data sets does not necessarily insulate researchers from quantitatively important finite-sample biases. We suggest that the partial R² and the F statistic of the identifying instruments in the first-stage estimation are useful indicators of the quality of the IV estimates and should be routinely reported. [ABSTRACT FROM AUTHOR]

Comments on a paper by Christian P. Robert, J.T. Gene Hwang, and William E. Strawderman published in the "Journal of the American Statistical Association" on the Pitman closeness criterion (PCC). Incompatibility of the criterion with the usual theoretic analysis; Dependency of the criterion's value on the joint distribution of two competing estimators; Role of PCC in decision theory.

Many authors have dealt with the problem of extending ordinary two-sample rank tests to cases where censoring occurs. Albers and Akritas (1987) proposed simple tests for this purpose, based on the idea of making separate rankings for uncensored and censored observations and subsequently combining the resulting two rank statistics. Similarly, some papers appeared that studied the problem of censoring for the paired-data case rather than the two-sample case. This article indicates how the approach of Albers and Akritas can be adapted to the paired-data case. The tests involved are not based on the ranks of the differences, but the differences of the ranks in the pooled sample. In this way, use is made of interblock information. The asymptotic distribution of the new statistic is obtained under the null hypothesis and contiguous location alternatives. By way of example, Wilcoxon- and Savage-type scores are introduced that are optimal for logistic location and exponential scale alternatives, respectively. The two corresponding tests are applied to some real data, and comparisons are made with the results obtained with competing tests on the same data set. [ABSTRACT FROM AUTHOR]

The minimum norm quadratic unbiased estimator type (MINQUE type) of estimates considered in this article are obtained by requiring identical values for the ratios of the a priori variances to the a priori error variance and letting this common value tend to infinity. The resulting estimates are invariant quadratic unbiased estimators with certain parametric and nonparametric optimality properties: assuming normally distributed random effects the efficiency of the proposed estimates to the minimum variance quadratic unbiased estimates (MIVQUE's) approaches unity when the true variance ratios are identical and tend to infinity. Assuming nonnormal effect distributions in the model with two variance components, the estimates are asymptotically efficient: in a sequence of designs where the number of classes and the number of observations on each class approach infinity, it is shown that the asymptotic variances of the estimates are equivalent to the theoretical minimum variances for invariant quadratic unbiased estimators. The result is interesting and useful since the usual analysis of variance (ANOVA) estimate of between-classes variance has strictly larger asymptotic variance for the unbalanced one-way model. Commonly considered estimates result from this procedure; the usual residual mean square (assuming that all non-error effects are fixed) is the resulting estimate of the error variance. In the one-way model the resulting estimates coincide with estimates considered by Thomas and Hultquist (1978), Burdick and Graybill (1984), Ahrens, Kleffe, and Tenzler (1981), and Kaplan (1982). In particular, the convergence of the MINQUE estimates was proved in the latter two papers in the context of the unbalanced one-way model. The procedure yields computationally convenient estimates in the general mixed ANOVA model. Computing formulas are... [ABSTRACT FROM AUTHOR]