bluuebunny/crossref_metadata_2025 huggingface dataset:
Parameter Estimation under Constraints for Multivariate Normal Distributions with Incomplete Data
Authors: Zoppé Alice , Anne Buu Yuh-Pey , Flury Bernard
Published: June 2007

This work presents an application of the EM-algorithm to two problems of estimation and testing in a multivariate normal distribution with missing data. The assumptions are that the observations are multivariate normally distributed and that the missing values are missing at random. The two models are tested applying the log-likelihood ratio test; for deriving the maximum likelihood estimates and evaluating the corresponding log-likelihood functions the EM algorithm is used. The problem of different and non-monotone patterns of missing data is solved introducing suitable transformations and partitions of the data matrix. The algorithm is proposed for general constraints on the mean vector; the topic of exchangeability of random vectors is also presented.
Accommodating Missing Data in Mixture Models for Classification by Opinion-Changing Behavior
Authors: Hill Jennifer L.
Published: June 2007

Popular theories in political science regarding opinion-changing behavior postulate the existence of one or both of two broad categories of people: those with stable opinions over time; and those who appear to hold no solid opinion and, when asked to make a choice, do so seemingly at random. The model presented here explores evidence for a third category: durable changers. People in this group will change their opinions in a rational, informed manner, after being exposed to new information. Survey data collected at four time points over nearly two years track Swiss citizens' readiness to support pollution-reduction policies. We analyzed the data using finite mixture models that allow estimation of the percentage in the poluation falling in each category for each question as well as the frequency of certain types of relevant behaviors within each category. These models extend the finite mixture model structure used in Hill and Kriesi (2001a,b) to accommodate missing response data. This extension increases the sample size by nearly 60% and weakens the missing-data assumptions required. We describe augmented models and fitting algorithms corresponding to different assumptions about the missing-data mechanism as well as the differences in results obtained.
A Rasch Hierarchical Measurement Model
Authors: Maier Kimberly S.
Published: June 2007

In this article, a hierarchical measurement model is developed that enables researchers to measure a latent trait variable and model the error variance corresponding to multiple levels. The Rasch hierarchical measurement model (HMM) results when a Rasch IRT model and a one-way ANOVA with random effects are combined ( Bryk & Raudenbush, 1992 ; Goldstein, 1987 ; Rasch, 1960 ). This model is appropriate for modeling dichotomous response strings nested within a contextual level. Examples of this type of structure include responses from students nested within schools and multiple response strings nested within people. Model parameter estimates of the Rasch HMM were obtained using the Bayesian data analysis methods of Gibbs sampling and the Metropolis-Hastings algorithm ( Gelfand, Hills, Racine-Poon, & Smith, 1990 ; Hastings, 1970 ; Metropolis, Rosenbluth, Rosenbluth, Teller, & Teller, 1953 ). The model is illustrated with two simulated data sets and data from the Sloan Study of Youth and Social Development. The results are discussed and parameter estimates for the simulated data sets are compared to parameter estimates obtained using a two-step estimation approach.
A Mixture Item Response Model for Multiple-Choice Data
Authors: Bolt Daniel M. , Cohen Allan S. , Wollack James A.
Published: June 2007

A mixture item response model is proposed for investigating individual differences in the selection of response categories in multiple-choice items. The model accounts for local dependence among response categories by assuming that examinees belong to discrete latent classes that have different propensities towards those responses. Varying response category propensities are captured by allowing the category intercept parameters in a nominal response model ( Bock, 1972 ) to assume different values across classes. A Markov Chain Monte Carlo algorithm for the estimation of model parameters and classification of examinees is described. A real-data example illustrates how the model can be used to distinguish examinees that are disproportionately attracted to different types of distractors in a test of English usage. A simulation study evaluates item parameter recovery and classification accuracy in a hypothetical multiple-choice test designed to be diagnostic. Implications for test construction and the use of multiple-choice tests to perform cognitive diagnoses of item response patterns are discussed.
Detecting a Change in School Performance: A Bayesian Analysis for a Multilevel Join Point Problem
Authors: Thum Yeow Meng , Bhattacharya Suman K.
Published: June 2007

A substantial literature on switches in linear regression functions considers situations in which the regression function is discontinuous at an unknown value of the regressor, Xk , where k is the so-called unknown “change point.” The regression model is thus a two-phase composite of yi ∼ N(β01 + β11xi, σ12), i=1, 2,..., k and yi ∼ N(β02 + β12xi, σ22), i= k + 1, k + 2,..., n. Solutions to this single series problem are considerably more complex when we consider a wrinkle frequently encountered in evaluation studies of system interventions, in that a system typically comprises multiple members (j = 1, 2, . . . , m ) and that members of the system cannot all be expected to change synchronously. For example, schools differ not only in whether a program, implemented system-wide, improves their students’ test scores, but depending on the resources already in place, schools may also differ in when they start to show effects of the program. If ignored, heterogeneity among schools in when the program takes initial effect undermines any program evaluation that assumes that change points are known and that they are the same for all schools. To describe individual behavior within a system better, and using a sample of longitudinal test scores from a large urban school system, we consider hierarchical Bayes estimation of a multilevel linear regression model in which each individual regression slope of test score on time switches at some unknown point in time, kj. We further explore additional results employing models that accommodate case weights and shorter time series.
Visions and Re-Visions of Charles Joseph Minard
Authors: Friendly Michael
Published: June 2007

Charles Joseph Minard is most widely known for a single work—his poignant flow-map depiction of the fate of Napoleon’s Grand Army in the disastrous Russian campaign of 1812. In fact, Minard was a true pioneer in thematic cartography and in statistical graphics; he developed many novel graphic forms to depict data, always with the goal to let the data “speak to the eyes.” This article reviews Minard’s contributions to statistical graphics, the time course of his work, and some background behind the famous March on Moscow graphic. This article also looks at some modern revisions of this graph from an information visualization perspective and examines some lessons this graphic provides as a test case for the power and expressiveness of computer systems or languages for graphic information display and visualization.
Modeling Sustained Educational Change With Panel Data: The Case for Dynamic Multiplier Analysis
Authors: Kaplan David
Published: June 2007

This article considers the problem of modeling sustained educational change via the use of dynamic multipliers applied to panel data. Dynamic multipliers arise from the incorporation of lagged endogenous variables in linear models. Three types of dynamic multipliers can be defined: (a) the impact multiplier, (b) interim multipliers, and (c) the long-run equilibrium multiplier. An impact multiplier gives the effect of a unit increase in an exogenous variable on an endogenous variable in the particular sample period. An interim multiplier gives the effect of a unit increase in an exogenous variable on an endogenous variable when that effect is sustained for a specified amount of time. A long-run equilibrium multiplier gives the effect of a unit increase in an exogenous variable on an endogenous variable when sustained into the indefinite future. This article seeks to develop and advocate dynamic multiplier analysis for education research. Extensions to multivariate dynamic linear models and multilevel linear models are provided. Three examples are presented to illustrate the methodology. The article closes with a discussion of the implications of dynamic multiplier analysis for education policy analysis.
Sensitivity Analysis for Hierarchical Models Employing t Level-1 Assumptions
Authors: Seltzer Michael , Novak John , Choi Kilchan , Lim Nelson
Published: June 2007

Much work on sensitivity analysis for hierarchical models (HMs) has focused on level-2 outliers (e.g., in multisite evaluations, a site at which an intervention was unusually successful). However, efforts to draw sound conclusions concerning parameters of interest in HMs also require that we attend to extreme level-1 units (e.g., a person in the treatment group at a particular site whose post-test score [yij ] is unusually small vis-á-vis the other members of that person’s group). One goal of this article is to examine the ways in which level-1 outliers can impact the estimation of fixed effects and random effects in HMs. A second goal is to outline and illustrate the use of Markov Chain Monte Carlo algorithms for conducting sensitivity analyses under t level-1 assumptions, including algorithms for settings in which the degrees of freedom at level 1 (v1 ) is treated as an unknown parameter.
A New Maximum Likelihood Estimator for the Population Squared Multiple Correlation
Authors: Alf Edward F. , Graf Richard G.
Published: June 2007

Using maximum likelihood estimation as described by R. A. Fisher (1912) , a new estimator for the population squared multiple correlation was developed. This estimator ( ρcirc;2(ML) ) was derived by examining all possible values of the population squared multiple correlation for a given sample size and number of predictors, and finding the one for which the observed sample value had the highest probability of occurring. The new estimator is shown to have greater accuracy than other estimators and to generate values that always fall within the parameter space. The utility of ρcirc; 2(ML) in terms of providing the basis for the development of small sample significance tests is demonstrated. A Microsoft Excel workbook for computing ρcirc; 2(ML) and its regions of nonsignificance and for computing a normal transformation for R2 is offered.
Uncertainty in Rank Estimation: Implications for Value-Added Modeling Accountability Systems
Authors: Lockwood J.R. , Louis Thomas A. , McCaffrey Daniel F.
Published: June 2007

Accountability for public education often requires estimating and ranking the quality of individual teachers or schools on the basis of student test scores. Although the properties of estimators of teacher-or-school effects are well established, less is known about the properties of rank estimators. We investigate performance of rank (percentile) estimators in a basic, two-stage hierarchical model capturing the essential features of the more complicated models that are commonly used to estimate effects. We use simulation to study mean squared error (MSE) performance of percentile estimates and to find the operating characteristics of decision rules based on estimated percentiles. Each depends on the signal-to-noise ratio (the ratio of the teacher or school variance component to the variance of the direct, teacher- or school-specific estimator) and only moderately on the number of teachers or schools. Results show that even when using optimal procedures, MSE is large for the commonly encountered variance ratios, with an unrealistically large ratio required for ideal performance. Percentile-specific MSE results reveal interesting interactions between variance ratios and estimators, especially for extreme percentiles, which are of considerable practical import. These interactions are apparent in the performance of decision rules for the identification of extreme percentiles, underscoring the statistical and practical complexity of the multiple goal inferences faced in value-added modeling. Our results highlight the need to assess whether even optimal percentile estimators perform sufficiently well to be used in evaluating teachers or schools.
Sample Size Requirements for Testing and Estimating Coefficient Alpha
Authors: Bonett Douglas G.
Published: June 2007

An approximate test and confidence interval for coefficient alpha are derived. The approximate test and confidence interval are then used to derive closed-form sample size formulas. The sample size formulas can be used to determine the sample size needed to test coefficient alpha with desired power or to estimate coefficient alpha with desired precision. The sample size formulas closely approximate the sample size requirements for an exact confidence interval or an exact test.
Estimation of Intervention Effects with Noncompliance: Alternative Model Specifications
Authors: Jo Booil
Published: June 2007

This study examines alternative ways of specifying models in the complier average causal effect (CACE) estimation method, where the major interest is in estimating causal effects of treatments for compliers. A fundamental difficulty involved in the CACE estimation method is in dealing with missing compliance information among study participants. Given that, the assumption of the exclusion restriction plays a critical role in separating the distributions of compliers and non-compliers. If no pretreatment covariates are available, assuming the exclusion restriction is unavoidable to obtain unique ML estimates in CACE models, although the assumption can be often unrealistic. One disadvantage of assuming the exclusion restriction is that the CACE estimate can be biased if the assumption is violated. Another disadvantage is that the assumption limits the flexibility of CACE modeling in practice. However, if pretreatment covariates are available, more modeling options other than strictly forcing the exclusion restriction can be considered to establish identifiability of CACE models. This study explores modeling possibilities of CACE estimation within an ML-EM framework in the presence of covariate information.
Statistical Inference for a Ratio of Dispersions Using Paired Samples
Authors: Bonett Douglas G. , Seier Edith
Published: June 2007

Wilcox (1990) examined the Type I and Type II error rates for several robust tests of H0: σ12/σ22 = 1 in paired-data designs and concluded that a satisfactory solution does not yet exist. A confidence interval for a ratio of correlated mean absolute deviations is derived and performs well in small sample sizes across realistically nonnormal distributions. When used to test a hypothesis, the proposed confidence interval is almost as powerful as the most powerful test examined by Wilcox.
Alternative Specifications of Multivariate Multilevel Probit Ordinal Response Models
Authors: Grilli Leonardo , Rampichini Carla
Published: June 2007

Multivariate multilevel models for ordinal variables are quite complex with respect to both interpretation and estimation. The specification in terms of a multivariate latent distribution and a set of thresholds helps in the interpretation of the variance-covariance parameters. However, most existing estimation algorithms for multilevel models can be used only if the model is reparameterized as a univariate model with an additional dummy bottom level. Moreover, the univariate formulation allows the model to be cast in the framework of Generalized Linear Latent and Mixed Models ( Rabe-Hesketh, Pickles, & Skrondal, 2001a ), a rather general class that includes, as special cases, structural equations and factor models. This article outlines the multivariate latent distribution specification and the corresponding interpretation issues; it then shows the univariate formulation, along with some alternative parameterizations that are useful in the estimation phase. An application to student ratings data illustrates the interpretation of the parameters and the estimation procedures, with a discussion of some computational issues.
The Resolution of Some Paradoxes Related to Reliability and Validity
Authors: Li Heng
Published: June 2007

Attention is brought to the existence of incongruity in the definition of reliability in the literature, and some unnecessary controversies and confusions which result. A conventionalization of the meaning of reliability at a conceptual, model-free level is proposed. Through the exploration of two paradoxes, this article attempts to address some potential misconceptions about reliability, validity, and psychometrics.
Maximum Likelihood Estimation of Nonlinear Structural Equation Models with Ignorable Missing Data
Authors: Lee Sik-Yum , Song Xin-Yuan , Lee John C. K.
Published: June 2007

The existing maximum likelihood theory and its computer software in structural equation modeling are established on the basis of linear relationships among latent variables with fully observed data. However, in social and behavioral sciences, nonlinear relationships among the latent variables are important for establishing more meaningful models and it is very common to encounter missing data. In this article, an EM type algorithm is developed for maximum likelihood estimation of a general nonlinear structural equation model with ignorable missing data, which are missing at random with an ignorable mechanism. To avoid computation of the complicated multiple integrals involved in the conditional expectations, the E-step is completed by a hybrid algorithm that combines the Gibbs sampler and the Metropolis-Hastings algorithm; while the M-step is completed efficiently by conditional maximization. Standard errors of the maximum likelihood estimates are obtained via Louis’s formula. The methodology is illustrated with results obtained from a simulation study and a real data set with rather complicated missing patterns and a large number of missing entries.
Kernel-Based Discriminant Techniques for Educational Placement
Authors: Lin Miao-hsiang , Huang Su-yun , Chang Yuan-chin
Published: June 2007

This article considers the problem of educational placement. Several discriminant techniques are applied to a data set from a survey project of science ability. A profile vector for each student consists of five science-educational indictors. The students are intended to be placed into three reference groups: advanced, regular, and remedial. Various discriminant techniques, including Fisher’s discriminant analysis and kernel-based nonparametric discriminant analysis, are compared. The evaluation work is based on the leaving-one-out misclassification score. Results from the five school data sets and 500 bootstrap samples reveal that the kernel-based nonparametric approach with bandwidth selected by cross validation performs reasonably well. The authors regard kernel-based nonparametric procedures as desirable competitors to Fisher’s discriminant rule for handling problems of educational placement.
The Superiority of Simple Alternatives to Regression for Social Science Predictions
Authors: Dana Jason , Dawes Robyn M.
Published: June 2007

Some simple, nonoptimized coefficients (e.g., correlation weights, equal weights) were pitted against regression in extensive prediction competitions. After drawing calibration samples from large supersets of real and synthetic data, the researchers observed which set of sample-derived coefficients made the best predictions when applied back to the superset. When adjusted R from the calibration sample was < .6, correlation weights were typically superior to regression coefficients, even if the sample contained 100 observations per predictor; unit weights were likewise superior to all methods if adjusted R was < .4. Correlation weights were generally the best method. It was concluded that regression is rarely useful for prediction in most social science contexts.
An Introduction to Generalized Estimating Equations and an Application to Assess Selectivity Effects in a Longitudinal Study on Very Old Individuals
Authors: Ghisletta Paolo , Spini Dario
Published: June 2007

Correlated data are very common in the social sciences. Most common applications include longitudinal and hierarchically organized (or clustered) data. Generalized estimating equations (GEE) are a convenient and general approach to the analysis of several kinds of correlated data. The main advantage of GEE resides in the unbiased estimation of population-averaged regression coefficients despite possible misspecification of the correlation structure. This article aims to provide a concise, nonstatistical introduction to GEE. To illustrate the method, an analysis of selectivity effects in the Swiss Interdisciplinary Longitudinal Study on the Oldest Old is presented.
A Sharing Item Response Theory Model for Computerized Adaptive Testing
Authors: Segall Daniel O.
Published: June 2007

A new sharing item response theory (SIRT) model is presented that explicitly models the effects of sharing item content between informants and test takers. This model is used to construct adaptive item selection and scoring rules that provide increased precision and reduced score gains in instances where sharing occurs. The adaptive item selection rules are expressed as functions of the item’s exposure rate in addition to other commonly used properties (characterized by difficulty, discrimination, and guessing parameters). Based on the results of simulated item responses, the new item selection and scoring algorithms compare favorably to the Sympson–Hetter exposure control method. The new SIRT approach provides higher reliability and lower score gains in instances where sharing occurs.
A Note on Standard Errors for Survival Curves in Discrete-Time Survival Analysis
Authors: Zwick Rebecca , Sklar Jeffrey C.
Published: June 2007

Cox (1972) proposed a discrete-time survival model that is somewhat analogous to the proportional hazards model for continuous time. Efron (1988) showed that this model can be estimated using ordinary logistic regression software, and Singer and Willett (1993) provided a detailed illustration of a particularly flexible form of the model that includes one parameter per time period. This work has been expanded to show how logistic regression output can also be used to estimate the standard errors of the survival functions. This is particularly simple under the model described by Singer and Willett, when there are no predictors other than time.
Randomized Item Response Theory Models
Authors: Fox Jean-Paul
Published: June 2007

The randomized response (RR) technique is often used to obtain answers on sensitive questions. A new method is developed to measure latent variables using the RR technique because direct questioning leads to biased results. Within the RR technique is the probability of the true response modeled by an item response theory (IRT) model. The RR technique links the observed item response with the true item response. Attitudes can be measured without knowing the true individual answers. This approach makes also a hierarchical analysis possible, with explanatory variables, given observed RR data. All model parameters can be estimated simultaneously using Markov chain Monte Carlo. The randomized item response technique was applied in a study on cheating behavior of students at a Dutch University. In this study, it is of interest if students’ cheating behavior differs across studies and if there are indicators that can explain differences in cheating behaviors.
Johnson–Neyman Type Technique in Hierarchical Linear Models
Authors: Miyazaki Yasuo , Maier Kimberly S.
Published: June 2007

In hierarchical linear models we often find that group indicator variables at the cluster level are significant predictors for the regression slopes. When this is the case, the average relationship between the outcome and a key independent variable are different from group to group. In these settings, a question such as “what range of the independent variable is the difference in the outcome variable statistically significant among groups?” naturally arises. The Johnson–Neyman (J-N) technique answers this kind of question in the analysis of covariance (ANCOVA) settings. In the hierarchical modeling context, the F test, which is widely used in ANCOVA, cannot be applied because the assumption of homogeneity of variance within cluster units is violated. Instead, the approximate Wald test can be used to determine the region of significance. To illustrate the application of the J-N technique in the context of hierarchical linear modeling, an example from research in education is provided.
Making the Most of What We Have: A Practical Application of Multidimensional Item Response Theory in Test Scoring
Authors: de la Torre Jimmy , Patz Richard J.
Published: June 2007

This article proposes a practical method that capitalizes on the availability of information from multiple tests measuring correlated abilities given in a single test administration. By simultaneously estimating different abilities with the use of a hierarchical Bayesian framework, more precise estimates for each ability dimension are obtained. The efficiency of the proposed method is most pronounced when highly correlated abilities are estimated from multiple short tests. Employing Markov chain Monte Carlo techniques allows for straightforward estimation of model parameters.
Assessing and Explaining Differential Item Functioning Using Logistic Mixed Models
Authors: Van den Noortgate Wim , De Boeck Paul
Published: June 2007

Although differential item functioning (DIF) theory traditionally focuses on the behavior of individual items in two (or a few) specific groups, in educational measurement contexts, it is often plausible to regard the set of items as a random sample from a broader category. This article presents logistic mixed models that can be used to model uniform DIF, treating the item effects and their interaction with groups (DIF) as random. In a similar way, the group effects can be modeled as random instead of fixed, if the groups can be considered a random sample from a population of groups. The models can, furthermore, be adapted easily for modeling DIF over individual persons rather than over groups, or for modeling the differential functioning of groups of items instead of individual items. It is shown that the logistic mixed model approach is not only a comprehensive and economical way to detect these different kinds of DIF, it also encourages us to explore possible explanations of DIF by including group or item covariates in the model.
A Hierarchical Linear Model With Factor Analysis Structure at Level 2
Authors: Miyazaki Yasuo , Frank Kenneth A.
Published: June 2007

In this article the authors develop a model that employs a factor analysis structure at Level 2 of a two-level hierarchical linear model (HLM). The model (HLM2F) imposes a structure on a deficient rank Level 2 covariance matrix (τ), and facilitates estimation of a relatively large τ matrix. Maximum likelihood estimators are derived via the Fisher scoring algorithm. An application of HLM2F to early vocabulary growth data illustrates the utility of the methodology. The methodology shown in this article has several advantages over the current two-level HLM (HLM2). Those include: (a) a parsimonious model that facilitates interplay between the theory and empirical results, (b) estimation of a deficient rank Level 2 covariance matrix, (c) calculation of change in deviance to evaluate the HLM2F model compared to HLM2, (d) construction of the distribution of a set of random effects in terms of an underlying latent factor, and (e) more efficient estimates for the variance component parameters.
A Lognormal Model for Response Times on Test Items
Authors: van der Linden Wim J.
Published: June 2007

A lognormal model for the response times of a person on a set of test items is investigated. The model has a parameter structure analogous to the two-parameter logistic response models in item response theory, with a parameter for the speed of each person as well as parameters for the time intensity and discriminating power of each item. It is shown how these parameters can be estimated by a Markov chain Monte Carlo method (Gibbs sampler). The method was used to analyze response times for the adaptive version of a test from the Armed Services Vocational Aptitude Battery. The same data set was used to test the validity of the model against a normal model using posterior predictive checks on the response times. The lognormal model showed an excellent fit to the data, whereas the normal model seemed unable to allow for a characteristic skewness of the response time distributions. The addition of an equality constraint on the discrimination parameters led only to a slight loss of fit. The potential use of the model for improving the daily practice of testing is indicated.
An Empirical Bayes Approach to Subscore Augmentation: How Much Strength Can We Borrow?
Authors: Edwards Michael C. , Vevea Jack L.
Published: June 2007

This article examines a subscore augmentation procedure. The approach uses empirical Bayes adjustments and is intended to improve the overall accuracy of measurement when information is scant. Simulations examined the impact of the method on subscale scores in a variety of realistic conditions. The authors focused on two popular scoring methods: summed scores and item response theory scale scores for summed scores. Simulation conditions included number of subscales, length (hence, reliability) of subscales, and the underlying correlations between scales. To examine the relative performance of the augmented scales, the authors computed root mean square error, reliability, percentage correctly identified as falling within specific proficiency ranges, and the percentage of simulated individuals for whom the augmented score was closer to the true score than was the nonaugmented score. The general findings and limitations of the study are discussed and areas for future research are suggested.
Detecting Answer Copying When the Regular Response Process Follows a Known Response Model
Authors: van der Linden Wim J. , Sotaridona Leonardo
Published: June 2007

A statistical test for detecting answer copying on multiple-choice items is presented. The test is based on the exact null distribution of the number of random matches between two test takers under the assumption that the response process follows a known response model. The null distribution can easily be generalized to the family of distributions of the number of random matches under the alternative hypothesis of answer copying. It is shown how this information can be used to calculate such features as the maximum, minimum, and expected values of the power function of the test. For the case of the nominal response model, the test is an alternative to the one based on statistic ω. The differences between the two tests are discussed and illustrated using empirical results.
Examination of a Simple Errors-in-Variables Model: A Demonstration of Marginal Maximum Likelihood
Authors: Camilli Gregory
Published: June 2007

A simple errors-in-variables regression model is given in this article for illustrating the method of marginal maximum likelihood (MML). Given suitable estimates of reliability, error variables, as nuisance variables, can be integrated out of likelihood equations. Given the closed form expression of the resulting marginal likelihood, the effects of error can be more clearly demonstrated. Derivations are given in detail to provide a detailed example of the marginalization strategy, and to prepare students for understanding more advanced applications of MML.
Estimation of the Correlation Coefficient Based on Selected Data
Authors: Hägglund Gösta , Larsson Rolf
Published: June 2007

In psychometrics, it is often the case that one encounters data that may not be considered random but selected in a systematic way according to some explanatory variable. In this article, maximum likelihood estimation is considered when data are supposed to arise from a bivariate normal distribution that is truncated in an extreme way. Two methods are presented and compared, one of them being purely numerical, while the other is based on an approximation. Both methods are tried on both simulated and on real data. The purely numerical method is shown to be the most reliable over all, but in some cases, the computationally less burdensome approximate method turns out to work almost as well.
Les rendements de la formation en entreprise
Authors: et Marie-Pierre Pelletier Benoit Dostie
Published: June 2007

This article studies the impact of firm-sponsored training on productivity. The data used are taken from the Workplace and Employee Survey (WES) of Statistics Canada (1999–2002). The impact of training on productivity is measured by estimating a Cobb-Douglas production function, where investment in human capital is treated as a production input. Our methodology is distinguished from that used in earlier studies by the fact that we model the firm's decision to offer training or not as well as the effect of training on productivity, which allows us to take into account the selection bias associated with training decisions. Our results permit us to conclude that training designated as “formal” provides higher productivity gains than training regarded as informal. We note also that failure to account for selection bias results in an overestimation of the effects of formal and informal training on the productivity of firms.
Minority Earnings Disparity Across the Distribution
Authors: Pendakur Krishna , Pendakur Ravi
Published: June 2007

We use quantile regression methods on 2001 Census of Canada data to assess disparity at four points in the conditional distribution of earnings of native-born ethnic minorities (the 20th, 50th, 80th and 90th percentiles) as well as at the mean. In doing so, we examine and assess the degree to which minorities face earnings differentials at both the top and bottom of the conditional distribution as well as at the mean, thereby testing the degree to which the mean difference is representative of differences across the distribution. We consider glass ceilings for Canadian-born ethnic minorities, and find evidence that some groups, such as Chinese-origin people, do indeed face more earnings disparity at the top of the distribution. However, other groups face different structures. South Asian-origin workers face greater disparity at the bottom than at the top, and Black workers face great disparity across the distribution. We interpret these latter patterns as identifying poor access of minority workers to good jobs in various parts of the distribution, rather than as negating a glass ceiling.
Risk for cancer in patients with Bardet‐Biedl syndrome and their relatives
Authors: Hjortshøj Tina Duelund , Grønskov Karen , Rosenberg Thomas , Brøndum‐Nielsen Karen , Olsen Jørgen H.
Published: June 2007

Bardet‐Biedl syndrome (BBS) is a rare, autosomal recessive disease with retinal dystrophy leading to blindness, postaxial polydactyly, truncal obesity, learning disabilities, male hypogenitalism, and renal anomalies. Heterozygous carriers of a BBS mutation are not thought to present symptoms of BBS; however, a previous study reported an increased risk of renal cancer among relatives of patients with BBS. This finding was based on the identification of three parents with renal cell carcinoma, representing a 17‐fold increased risk. We performed a population‐based study in Denmark to examine the incidence of cancer in 116 BBS patients and 428 relatives (96 families) through record linkage of information from files of the Retinitis Pigmentosa Registry, the Central Population Registry, and the Danish Cancer Registry. The clinical diagnosis of BBS was molecularly confirmed in 52% of the patients. Among the patients, two cancers were reported, with 4.3 expected. The cancers were an embryonal carcinoma of the testis in a 23‐year‐old man and an acoustic neuroma in a 51‐year‐old man. Among the relatives, 30 cancers were observed, with 45.2 expected. No renal cancers were observed in the two groups. These data do not support the suggested increased risk for renal cancer in relatives of patients with BBS. © 2007 Wiley‐Liss, Inc.
Gespannte Metallocenophane und ähnliche metallorganische Ringe mit π‐Kohlenwasserstoffliganden und Übergangsmetallzentren
Authors: Herbert David E. , Mayer Ulrich F. J. , Manners Ian
Published: June 2007

Die Strukturen, Bindungsverhältnisse und Ringöffnungsreaktionen gespannter cyclischer Kohlenstoffverbindungen sind ein fester Bestandteil des Lehrbuchwissens. Gespannte metallorganische Verbindungen mit Übergangsmetallzentren blicken hingegen auf eine viel kürzere Geschichte zurück: Erst jüngere Arbeiten zu ihrer Synthese und ihren Eigenschaften deckten faszinierende Aspekte ihrer Struktur, Bindungsverhältnisse und Reaktivität auf. Auf der Grundlage der Untersuchungen zu gespannten Ferrocenophanen wurden vielfältige gespannte metallorganische Ringe mit unterschiedlichen Metallzentren, π‐Kohlenwasserstoffliganden und Brücken entwickelt. Solche gespannten Spezies können unter Ringöffnung an Oberflächen binden (und diese funktionalisieren) oder durch Ringöffnungspolymerisationen leicht verarbeitbare Metallopolymere bilden. Dieser Aufsatz fasst den aktuellen Stand der Synthese und Strukturbestimmung sowie die Kenntnisse über die elektronische Struktur und die Reaktivität gespannter metallorganischer Ringe mit π‐Kohlenwasserstoffliganden und d‐Block‐Metallen zusammen.
Electrospinning: A Fascinating Method for the Preparation of Ultrathin Fibers
Authors: Greiner Andreas , Wendorff Joachim H.
Published: June 2007

Electrospinning is a highly versatile method to process solutions or melts, mainly of polymers, into continuous fibers with diameters ranging from a few micrometers to a few nanometers. This technique is applicable to virtually every soluble or fusible polymer. The polymers can be chemically modified and can also be tailored with additives ranging from simple carbon‐black particles to complex species such as enzymes, viruses, and bacteria. Electrospinning appears to be straightforward, but is a rather intricate process that depends on a multitude of molecular, process, and technical parameters. The method provides access to entirely new materials, which may have complex chemical structures. Electrospinning is not only a focus of intense academic investigation; the technique is already being applied in many technological areas.
Determination of sirolimus in rabbit arteries using liquid chromatography separation and tandem mass spectrometric detection
Authors: Zhang Jun , Rodila Ramona , Watson Pamela , Ji Qin , El‐Shourbagy Tawakol A.
Published: June 2007

Sirolimus, an effective immunosuppressive agent, is used for drug eluting stents. During stent development, an analytical method for the determination of sirolimus in tissue needs to be established. Normally, tissue samples are homogenized and then analyzed against the calibration standards prepared in a tissue homogenate. This approach provides insufficient control of the homogenization process. In this paper, tissue quality control samples were introduced for the optimization of the homogenization process during method development, but also allowance for the performance evaluation of the entire analytical process. In addition, a new approach using rabbit blood as a homogenization medium was developed to stabilize sirolimus in rabbit tissue homogenates. Calibration standards and quality controls were prepared by spiking different sirolimus working solutions into rabbit blood. Homogenization quality control samples were prepared by injecting other sirolimus working solutions into empty test tubes and pre‐cut arteries within pre‐defined masses. A high‐throughput homogenization procedure was optimized based on the specific chemical properties of sirolimus. The linear dynamic range was between 49.9 pg/mL and 31.9 ng/mL to accommodate the expected artery homogenate concentrations. Additionally, quality controls in rabbit blood were also used in the extraction to support the calibration standards. The accuracy and precision of the quality controls in rabbit blood reflect the extraction performance and the accuracy and precision of the homogenization tissue quality controls reflect the overall performance of the method. The mean bias was between −4.5 and 0.2% for all levels of quality controls in the blood and between 4.8 and 14.9% for all levels of the homogenization tissue quality controls. The CVs of all concentration levels were ≤5.3% for the quality controls in blood and ≤9.2% for the homogenization tissue quality controls. The method was successfully applied to determine the concentration of sirolimus in the rabbit arteries. Copyright © 2007 John Wiley & Sons, Ltd.
LC‐MS/MS determination of 2‐(4‐((2‐(2S,5R)‐2‐Cyano‐5‐ethynyl‐1‐pyrrolidinyl)‐2‐oxoethylamino)‐4‐methyl‐1‐piperidinyl)‐4‐pyridinecarboxylic acid (ABT‐279) in dog plasma with high‐throughput protein precipitation sample preparation
Authors: Kim Joseph , Flick Jeanette , Reimer Michael T. , Rodila Ramona , Wang Perry G. , Zhang Jun , Ji Qin C. , El‐Shourbagy Tawakol A.
Published: June 2007

As an effective DPP‐IV inhibitor, 2‐(4‐((2‐(2S,5R)‐2‐Cyano‐5‐ethynyl‐1‐pyrrolidinyl)‐2‐oxoethylamino)‐4‐methyl‐1‐piperidinyl)‐4‐pyridinecarboxylic acid (ABT‐279), is an investigational drug candidate under development at Abbott Laboratories for potential treatment of type 2 diabetes. In order to support the development of ABT‐279, multiple analytical methods for an accurate, precise and selective concentration determination of ABT‐279 in different matrices were developed and validated in accordance with the US Food and Drug Administration Guidance on Bioanalytical Method Validation. The analytical method for ABT‐279 in dog plasma was validated in parallel to other validations for ABT‐279 determination in different matrices. In order to shorten the sample preparation time and increase method precision, an automated multi‐channel liquid handler was used to perform high‐throughput protein precipitation and all other liquid transfers. The separation was performed through a Waters YMC ODS‐AQ column (2.0 × 150 mm, 5 µm, 120 Å) with a mobile phase of 20 mm ammonium acetate in 20% acetonitrile at a flow rate of 0.3 mL/min. Data collection started at 2.2 min and continued for 2.0 min. The validated linear dynamic range in dog plasma was between 3.05 and 2033.64 ng/mL using a 50 µL sample volume. The achieved r2 coefficient of determination from three consecutive runs was between 0.998625 and 0.999085. The mean bias was between −4.1 and 4.3% for all calibration standards including lower limit of quantitation. The mean bias was between −8.0 and 0.4% for the quality control samples. The precision, expressed as a coefficient of variation (CV), was ≤4.1% for all levels of quality control samples. The validation results demonstrated that the high‐throughput method was accurate, precise and selective for the determination of ABT‐279 in dog plasma. The validated method was also employed to support two toxicology studies. The passing rate was 100% for all 49 runs from one validation study and two toxicology studies. Copyright © 2007 John Wiley & Sons, Ltd.
Quantification of tenatoprazole in rat plasma by HPLC: validation and its application to pharmacokinetic studies
Authors: Nirogi Ramakrishna , Kandikere Vishwottam , Mudigonda Koteshwara , Bhyrapuneni Gopinadh
Published: June 2007

A simple, reliable HPLC method with UV detection (295 nm) in rat plasma was developed and validated for quantification of tenatoprazole, a novel proton pump inhibitor, which is in clinical trials. Following a single‐step liquid–liquid extraction, the analyte and internal standard were separated using an isocratic mobile phase on a reverse phase C18 column. The lower limit of quantitation was 20 ng/mL, with a relative standard deviation of less than 10%. A linear dynamic range of 20–6000 ng/mL was established. This HPLC method was validated with between‐batch and within‐batch precision of 2.9–6.3 and 1.4–5.8%, respectively. The between‐batch and within‐batch accuracy was 95.1–104.1 and 92.4–101.0%, respectively. This validated method is simple and repeatable enough to be used in pharmacokinetic studies. Copyright © 2007 John Wiley & Sons, Ltd.
Facts and Challenges in the Understanding of the Biosynthesis of Peptaibols by Trichoderma
Authors: Kubicek Christian P. , Komoń‐Zelazowska Monika , Sándor Erzsébet , Druzhinina Irina S.
Published: June 2007

Species of the mitosporic filamentous fungal genus Trichoderma are prominent producers of both short (7–11 residues) and long (18–20 residues) peptaibols and peptaibiotics, which are thought to be involved in their interaction with other living systems. Numerous reviews are available regarding biodiversity, structure, and mode of action of these peptide derivatives, but little emphasis has been paid to the physiology and genetics of their formation. In this review article, we used the recent knowledge on biosynthesis and production of these components to speculate on some of the unknown points. We also highlight areas where further research is most urgently needed.
Total Synthesis in Solution and Conformational Analysis of the Peptaibol Cervinin and Selected Analogues
Authors: Baldini Chiara , Bellanda Massimo , Peggion Cristina , Djontu Albert Legrand , Atagua Come , Mammi Stefano , Toniolo Claudio
Published: June 2007

The total synthesis in solution and chemical characterization of the antibacterial undecamer peptaibol cervinin, its C‐terminal (Lol‐Ac) derivative, also naturally occurring, and three selected analogues modified at the N‐ and/or C‐terminus(i) to improve the affinity to the membrane environment, are described. A solution conformational analysis in different media, performed by the combined use of FT‐IR, CD, and 2D‐NMR techniques, clearly shows a significant 310‐like helicity for these Aib‐Pro rich peptides. The hydrophilicity/lipophilicity characteristics of the final compounds significantly influence their membrane‐modifying properties.
Synthesis, and Structural and Biological Studies of Efrapeptin C Analogues
Authors: Jost Micha , Weigelt Sven , Huber Thomas , Majer Zsuzsanna , Greie Jörg‐Christian , Altendorf Karlheinz , Sewald Norbert
Published: June 2007

A series of analogues of efrapeptin C (1), with variations in the central tripeptide epitope (positions 6–8), were prepared by a combination of solid‐ and solution‐phase peptide syntheses. The conformations of the modified compounds 2–6 were investigated by circular‐dichroism (CD) spectroscopy to differentiate between 310‐ and α‐helical secondary structures. The inhibitory activities of the new compounds towards F1‐ATPase from E. coli were determined. The modified congeners 3–5 were less active by one order of magnitude compared to 1 (Ki 10 μM), and 6 was completely inactive. Our experiments demonstrate that the flexible, central tripeptide epitope, comprising positions 6–8 in 1, is crucial for molecular recognition, even slight sequence modifications being hardly tolerated.