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This note discusses the radical technical inflation in error variance and the related standard error of test scores from both conceptual and empirical viewpoints. This technical inflation arises as a direct consequence of the technical underestimation of item-score correlation by the product–moment coefficient of correlation (PMC), which is embedded in the traditional estimators of reliability such as coefficients alpha, theta, omega, or rho (maximal reliability). Specifically, in educational settings where compilations usually include both easy and difficult items, the estimate by PMC may substantially deviate from the true association between an item and the score. Consequently, the use of traditional estimators of reliability leads to technically inflated estimates of standard errors, as the error variance related to these traditional measurement models is significantly inflated, resulting in deflated reliability estimates. In educational testing, employing deflation-corrected standard errors, calculated using deflation-corrected reliability estimators, would provide a more accurate measure of the test score's true precision. [ABSTRACT FROM AUTHOR]

The standard errors (SE) of the maximum likelihood estimates (MLE) of the population parameter vector in nonlinear mixed effect models (NLMEM) are usually estimated using the inverse of the Fisher information matrix (FIM). However, at a finite distance, i.e. far from the asymptotic, the FIM can underestimate the SE of NLMEM parameters. Alternatively, the standard deviation of the posterior distribution, obtained in Stan via the Hamiltonian Monte Carlo algorithm, has been shown to be a proxy for the SE, since, under some regularity conditions on the prior, the limiting distributions of the MLE and of the maximum a posterior estimator in a Bayesian framework are equivalent. In this work, we develop a similar method using the Metropolis-Hastings (MH) algorithm in parallel to the stochastic approximation expectation maximisation (SAEM) algorithm, implemented in the saemix R package. We assess this method on different simulation scenarios and data from a real case study, comparing it to other SE computation methods. The simulation study shows that our method improves the results obtained with frequentist methods at finite distance. However, it performed poorly in a scenario with the high variability and correlations observed in the real case study, stressing the need for calibration. [ABSTRACT FROM AUTHOR]

This note discusses the radical technical inflation in error variance and the related standard error of test scores from both conceptual and empirical viewpoints. This technical inflation arises as a direct consequence of the technical underestimation of item-score correlation by the product–moment coefficient of correlation (PMC), which is embedded in the traditional estimators of reliability such as coefficients alpha, theta, omega, or rho (maximal reliability). Specifically, in educational settings where compilations usually include both easy and difficult items, the estimate by PMC may substantially deviate from the true association between an item and the score. Consequently, the use of traditional estimators of reliability leads to technically inflated estimates of standard errors, as the error variance related to these traditional measurement models is significantly inflated, resulting in deflated reliability estimates. In educational testing, employing deflation-corrected standard errors, calculated using deflation-corrected reliability estimators, would provide a more accurate measure of the test score’s true precision.

Copyright of International Journal of Economics, Business & Politics (UEIP) is the property of International Journal of Economics, Business & Politics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

"Simpler standard errors for two-stage optimization estimators" (Terza, 2016a, Stata Journal 16: 368–385) offers an analytic simplification of the daunting textbook formulations of the asymptotic variance–covariance matrix of a class of two-stage optimization estimators. Here I revisit that simplification and show that it applies to a much broader class of estimators than was originally considered. I also offer a correction that further enhances the generality of this asymptotic variance–covariance matrix formulation. These points are illustrated via a real-data application. [ABSTRACT FROM AUTHOR]

Multidimensional forced-choice (MFC) tests are increasing in popularity but their construction is complex. The Thurstonian item response model (Thurstonian IRT model) is most often used to score MFC tests that contain dominance items. Currently, in a frequentist framework, information about the latent traits in the Thurstonian IRT model is computed for binary outcomes of pairwise comparisons, but this approach neglects stochastic dependencies. In this manuscript, it is shown how to estimate Fisher information on the block level. A simulation study showed that the observed and expected standard errors based on the block information were similarly accurate. When local dependencies for block sizes > 2 were neglected, the standard errors were underestimated, except with the maximum a posteriori estimator. It is shown how the multidimensional block information can be summarized for test construction. A simulation study and an empirical application showed small differences between the block information summaries depending on the outcome considered. Thus, block information can aid the construction of reliable MFC tests. [ABSTRACT FROM AUTHOR]

Item response theory models and applications are affected by many sources of variability, including errors associated with item parameter estimation. Metric stability analysis (MSA) is one method to evaluate the effects of item parameter standard errors that quantifies how well a model determines the latent trait metric. This paper describes how to evaluate MSA in dichotomous and polytomous data and describes a Bayesian implementation of MSA that does not require a positive definite variance–covariance matrix among item parameters. MSA analyses are illustrated in the context of an oral-health-related quality of life measure administered before and after prosthodontic treatment. The R code to implement the methods described in this paper is provided.

Item response theory models and applications are affected by many sources of variability, including errors associated with item parameter estimation. Metric stability analysis (MSA) is one method to evaluate the effects of item parameter standard errors that quantifies how well a model determines the latent trait metric. This paper describes how to evaluate MSA in dichotomous and polytomous data and describes a Bayesian implementation of MSA that does not require a positive definite variance–covariance matrix among item parameters. MSA analyses are illustrated in the context of an oral-health-related quality of life measure administered before and after prosthodontic treatment. The R code to implement the methods described in this paper is provided. [ABSTRACT FROM AUTHOR]

The linear regression model is widely used in empirical work in economics, statistics, and many other disciplines. Researchers often include many covariates in their linear model specification in an attempt to control for confounders. We give inference methods that allow for many covariates and heteroscedasticity. Our results are obtained using high-dimensional approximations, where the number of included covariates is allowed to grow as fast as the sample size. We find that all of the usual versions of Eicker–White heteroscedasticity consistent standard error estimators for linear models are inconsistent under this asymptotics. We then propose a new heteroscedasticity consistent standard error formula that is fully automatic and robust to both (conditional) heteroscedasticity of unknown form and the inclusion of possibly many covariates. We apply our findings to three settings: parametric linear models with many covariates, linear panel models with many fixed effects, and semiparametric semi-linear models with many technical regressors. Simulation evidence consistent with our theoretical results is provided, and the proposed methods are also illustrated with an empirical application. Supplementary materials for this article are available online.

A detailed examination of the 1930 Lanarkshire Milk Experiment (LME) by the famous statistician William Sealy Gossett ("Student"), which appeared in Biometrika in 1931, is re-examined from a more modern perspective. The LME had a complicated design whereby 67 schools in Lanarkshire were allocated to receive either raw or pasteurised milk but pupils within the schools were allocated to either receive milk or to act as controls. Student's criticisms are considered in detail and examined in terms of subsequent developments on the design and analysis of experiments, in particular as regards appropriate estimation of standard errors of treatment estimates when an incomplete blocks structure has been used. An analogy with a more modern trial in osteoarthritis is made. Suggestions are made as to how analysis might proceed if the original data were available. Some lessons for observational studies in epidemiology are drawn and it is speculated that hidden clustering structures might be an explanation as to why results may vary from observational study to observational study by more than conventionally calculated standard errors might suggest. [ABSTRACT FROM AUTHOR]

Reporting standard errors of equating has been advocated as a standard practice when conducting test equating. The two most widely applied procedures for standard errors of equating including the bootstrap method and the delta method are either computationally intensive or confined to the derivations of complicated formulas. In the current study, a hypothetical example was used to illustrate how the multiple imputation method could be taken as an alternative procedure for obtaining the standard errors for the item response theory (IRT) true score equating coefficients in the context of the common-item nonequivalent groups equating design under the three-parameter logistic IRT model. This method makes use of multiple sets of imputed item parameter values. By using the simulated and real data, the performance of the multiple imputation method was examined and compared with that of the bootstrap and delta methods. The results indicated that the multiple imputation method performed as effectively as the bootstrap method and the delta method when using the characteristic curve methods. The multiple imputation method produced very similar results to the delta method when the moment methods were used. [ABSTRACT FROM AUTHOR]

Each observed indicator HT ht is then defined by the multiple factor model ([4]) HT ht Graph where HT ht and HT ht are the factor loadings, HT ht denotes the item mean, and HT ht is a random error term with variance HT ht . In a bifactor model, HT ht and each HT ht are independent of each other and the error terms are independent of each other and with HT ht and each HT ht . [Extracted from the article]

Non-response in establishment surveys is a very important problem that can bias results of statistical analysis. The bias can be considerable when the survey data is used to do multivariate analysis that involve several variables with different response rates, which can reduce the effective sample size considerably. Fixing the non-response, however, could potentially cause other econometric problems. This paper uses an operational approach to analyze the sensitivity of results of multivariate analysis to multiple imputation procedures applied to the U.S. Census Bureau/NSF‘s Business Research and Development and Innovation Survey (BRDIS) to address item non-response. Multiple imputation is first applied using data from all survey units and periods for which there is data, presenting scenario 1. A scenario 2 involves separate imputation for units that have participated in the survey only once and those that repeat. Scenario 3 involves no imputation. Sensitivity analysis is done by comparing the model estimates and their standard errors, and measures of the additional uncertainty created by the imputation procedure. In all cases, unit non-response is addressed by using the adjusted weights that accompany BRDIS micro data. The results suggest that substantial benefit may be derived from multiple imputation, not only because it helps provide more accurate measures of the uncertainty due to item non-response but also because it provides alternative estimates of effect sizes and population totals.

Groundwater levels serve as a monitoring parameter of changes in groundwater storage and vulnerability status of coastal aquifers to seawater intrusion. Secondary piezometric levels datasets obtained in dry and wet seasons of 2017 from 12 dedicated observation wells in Ghiss-Nekor coastal plain were used to conduct this study. The measured data are reliable; however, investigated sampling locations are irregularly distributed and clustered along the northern half of the plain. Our research examined the performance of two linear kriging methods: empirical Bayesian kriging (EBK) and ordinary kriging (OK), regarding interpolation of this scattered dataset. Cross-validation results for assessing the prediction accuracy approved the selection of EBK as the best-fit method. By adopting interpolated EBK-based estimates, accurate groundwater levels distribution maps were developed enabling the delineation of zones at the highest risk of seawater intrusion or occurrence of slight piezometric recovery. Besides yielding satisfactory outcome for small number of observations, EBK technique generates standard errors associated with the predicted values; the latter were included in our study as a criterion for selecting locations of high uncertainty of estimates and requiring an increase in the number of wells. By considering the results of the Densify Sampling Network tool and the highest priority indices, 26 new wells are proposed to be added to the investigated observation network, with the possibility of excluding wells of least priority. Usage of DSN tool and priority index, relying respectively on the standard error of prediction surface and cross-validation residuals, is deemed satisfactory as the mean standard error diminished by over 60% after placing the additional monitoring sites. [ABSTRACT FROM AUTHOR]

In standardized testing, equating is used to ensure comparability of test scores across multiple test administrations. One equipercentile observed-score equating method is kernel equating, where an essential step is to obtain continuous approximations to the discrete score distributions by applying a kernel with a smoothing bandwidth parameter. When estimating the bandwidth, additional variability is introduced which is currently not accounted for when calculating the standard errors of equating. This poses a threat to the accuracy of the standard errors of equating. In this study, the asymptotic variance of the bandwidth parameter estimator is derived and a modified method for calculating the standard error of equating that accounts for the bandwidth estimation variability is introduced for the equivalent groups design. A simulation study is used to verify the derivations and confirm the accuracy of the modified method across several sample sizes and test lengths as compared to the existing method and the Monte Carlo standard error of equating estimates. The results show that the modified standard errors of equating are accurate under the considered conditions. Furthermore, the modified and the existing methods produce similar results which suggest that the bandwidth variability impact on the standard error of equating is minimal. [ABSTRACT FROM AUTHOR]

Most of agricultural, industrial and engineering experimentations involve multi-stratum experimental designs. The factors involved are randomised at each level, a process that leads to different levels of precision. Traditionally, the factors involved are clearly defined as either fixed or random in nature. In this paper, we consider factors where each of them has both fixed and random levels. We consider a split-split plot treatment structure where the main-plot factor is laid in a randomized complete block design (RCBD) and each of the factors has both fixed and random levels. Of interest is to measure some of the factors with a higher precision than the others. We formulate a linear mixed effects model, review the estimation process, construct and test hypotheses and assess the model adequacy. In principle, we extend the work of Njuho and Milliken (2009) to a split-split-plot structure with the main-plot factor laid in a RCBD. The findings provide for a broad-based conceptualisation process of the fixed and random levels of factors. It allows for narrow, intermediate and broad inference on levels of each of the factors and their associated interactions. We illustrate the concept using a practical example. [ABSTRACT FROM AUTHOR]

This paper extends the stochastic approach to Laspeyres price index number by augmenting the simple model with a systematic change in relative prices when the errors are serially correlated of order one. The estimators and their standard errors are derived using weighted least square (WLS) approach where two round estimation process is considered. The first round of estimation gives the estimators of extended Laspeyres price model. While the standard errors of estimators are obtained in second round of estimation. The performance of proposed method is presented by an illustrative application. [ABSTRACT FROM AUTHOR]

Researchers have developed a characteristic curve procedure to estimate the parameter scale transformation coefficients in test equating under the nominal response model. In the study, the delta method was applied to derive the standard error expressions for computing the standard errors for the estimates of the parameter scale transformation coefficients. This brief report presents the results of a simulation study that examined the accuracy of the derived formulas and compared the performance of this analytical method with that of the multiple imputation method. The results indicated that the standard errors produced by the delta method were very close to the criterion standard errors as well as those yielded by the multiple imputation method under all the simulation conditions. [ABSTRACT FROM AUTHOR]

The paper is framed within the literature around Louis' identity for the observed information matrix in incomplete data problems, with a focus on the implied acceleration of maximum likelihood estimation for mixture models. The goal is twofold: to obtain direct expressions for standard errors of parameters from the EM algorithm and to reduce the computational burden of the estimation procedure for a class of mixture models with uncertainty for rating variables. This achievement fosters the feasibility of best-subset variable selection, which is an advisable strategy to identify response patterns from regression models for all Mixtures of Experts systems. The discussion is supported by simulation experiments and a real case study. [ABSTRACT FROM AUTHOR]

Using Louis' formula, it is possible to obtain the observed information matrix and the corresponding large‐sample standard error estimates after the expectation–maximization (EM) algorithm has converged. However, Louis' formula is commonly de‐emphasized due to its relatively complex integration representation, particularly when studying latent variable models. This paper provides a holistic overview that demonstrates how Louis' formula can be applied efficiently to item response theory (IRT) models and other popular latent variable models, such as cognitive diagnostic models (CDMs). After presenting the algebraic components required for Louis' formula, two real data analyses, with accompanying numerical illustrations, are presented. Next, a Monte Carlo simulation is presented to compare the computational efficiency of Louis' formula with previously existing methods. Results from these presentations suggest that Louis' formula should be adopted as a standard method when computing the observed information matrix for IRT models and CDMs fitted with the EM algorithm due to its computational efficiency and flexibility. [ABSTRACT FROM AUTHOR]

O'Hara and Kotze (Methods Ecol Evol 1: 118–122, 2010) present simulation results that appear to show very poor behaviour (as judged by bias and overall accuracy) of linear models applied to count data, especially in relation to GLM analysis.We considered O'Hara and Kotze's (2010) comparisons, and determined that the finding occurred primarily because the quantity that they estimated in their simulations of the linear model analysis (the mean of a transformation of the count data) was not the same quantity that was simulated and to which the results were compared (the logarithm of the mean of the count data). We correct this discrepancy, re‐run O'Hara and Kotze's simulations and add additional simple analyses.We found that the apparent superiority of the GLMs over linear models in O'Hara and Kotze's (2010) simulations was primarily an artefact of divergence in the meanings of results from the two analyses. After converting results from linear model analyses of transformed data to estimators of the same quantity as provided by the GLM, results from both analyses rarely differed substantially. Furthermore, under the circumstances considered by O'Hara and Kotze, we find that an even simpler implementation of linear model analysis, inference of the mean of the raw data, performs even better and gives identical results to the GLM.While the analysis of count data with GLMs can certainly provide many benefits, we strongly caution against interpreting O'Hara and Kotze's (2010) results as evidence that simpler approaches are severely flawed. [ABSTRACT FROM AUTHOR]

Two-level Mokken scale analysis is a generalization of Mokken scale analysis for multi-rater data. The bias of estimated scalability coefficients for two-level Mokken scale analysis, the bias of their estimated standard errors, and the coverage of the confidence intervals has been investigated, under various testing conditions. It was found that the estimated scalability coefficients were unbiased in all tested conditions. For estimating standard errors, the delta method and the cluster bootstrap were compared. The cluster bootstrap structurally underestimated the standard errors of the scalability coefficients, with low coverage values. Except for unequal numbers of raters across subjects and small sets of items, the delta method standard error estimates had negligible bias and good coverage. Post hoc simulations showed that the cluster bootstrap does not correctly reproduce the sampling distribution of the scalability coefficients, and an adapted procedure was suggested. In addition, the delta method standard errors can be slightly improved if the harmonic mean is used for unequal numbers of raters per subject rather than the arithmetic mean. [ABSTRACT FROM AUTHOR]

For the construction of tests and questionnaires that require multiple raters (e.g., a child behaviour checklist completed by both parents) a novel ordinal scaling technique is currently being further developed, called two‐level Mokken scale analysis. The technique uses within‐rater and between‐rater coefficients to assess the scalability of the test. These coefficients are generalizations of Mokken's scalability coefficients. In this paper we derived standard errors for the two‐level coefficients and for their ratios. The coefficients, the estimates, the estimated standard errors and the software implementation are discussed and illustrated using a real‐data example, and a small‐scale simulation study demonstrates the accuracy of the estimates. [ABSTRACT FROM AUTHOR]

When a scanner is installed and begins to be used operationally, its actual performance may deviate somewhat from the predictions made at the design stage. Thus it is recommended that routine quality assurance (QA) measurements be used to provide an operational under-standing of scanning properties. While QA data are primarily used to evaluate sensitivity and bias patterns, there is a possibility to also make use of such data sets for amore refined understanding of the 3-D scanning properties. Building on some recent work on analysis of the distributional characteristics of iteratively reconstructed PET data, we construct an auto-regression model for analysis of the 3-D spatial auto-covariance structure of iteratively reconstructed data, after normalization. Appropriate likelihood-based statistical techniques for estimation of the auto-regression model coefficients are described. The fitted model leads to a simple process for approximate simulation of scanner performance—one that is readily implemented in an R script. The analysis provides a practical mechanism for evaluating the operational error characteristics of iteratively reconstructed PET images. Simulation studies are used for validation. The approach is illustrated on QA data from an operational clinical scanner and numerical phantom data. We also demonstrate the potential for use of these techniques, as a form of model-based bootstrapping, to provide assessments of measurement uncertainties in variables derived from clinical FDG-PET scans. This is illustrated using data from a clinical scan in a lung cancer patient,after a 3-minute acquisition has been re-binned into three consecutive 1-minute time-frames. An uncertainty measure for the tumor SUVmax value is obtained. The methodology is seen to be practical and could be a useful support for quantitative decision making based on PET data. [ABSTRACT FROM AUTHOR]

Researchers often make use of linear regression models in order to assess the impact of policies on target outcomes. In a correctly specified linear regression model, the marginal impact is simply measured by the linear regression coefficient. However, when dealing with both synchronic and diachronic spatial data, the interpretation of the parameters is more complex because the effects of policies extend to the neighboring locations. Summary measures have been suggested in the literature for the cross-sectional spatial linear regression models and spatial panel data models. In this article, we compare three procedures for testing the significance of impact measures in the spatial linear regression models. These procedures include (i) the estimating equation approach, (ii) the classical delta method, and (iii) the simulation method. In a Monte Carlo study, we compare the finite sample properties of these procedures. [ABSTRACT FROM AUTHOR]

Mixture models with (multivariate) Gaussian components are a popular tool in model-based clustering. Such models are often fitted by a procedure that maximizes the likelihood, such as the EM algorithm. At convergence, the maximum likelihood parameter estimates are typically reported, but in most cases little emphasis is placed on the variability associated with these estimates. In part this may be due to the fact that standard errors are not directly calculated in the model-fitting algorithm, either because they are not required to fit the model, or because they are difficult to compute. The examination of standard errors in model-based clustering is therefore typically neglected. Sampling based methods, such as the jackknife (JK), bootstrap (BS) and parametric bootstrap (PB), are intuitive, generalizable approaches to assessing parameter uncertainty in model-based clustering using a Gaussian mixture model. This paper provides a review and empirical comparison of the jackknife, bootstrap and parametric bootstrap methods for producing standard errors and confidence intervals for mixture parameters. The performance of such sampling methods in the presence of small and/or overlapping clusters requires consideration however; here the weighted likelihood bootstrap (WLBS) approach is demonstrated to be effective in addressing this concern in a model-based clustering framework. The JK, BS, PB and WLBS methods are illustrated and contrasted through simulation studies and through the traditional Old Faithful data set and also the Thyroid data set. The MclustBootstrap function, available in the most recent release of the popular R package mclust, facilitates the implementation of the JK, BS, PB and WLBS approaches to estimating parameter uncertainty in the context of model-based clustering. The JK, WLBS and PB approaches to variance estimation are shown to be robust and provide good coverage across a range of real and simulated data sets when performing model-based clustering; but care is advised when using the BS in such settings. In the case of poor model fit (for example for data with small and/or overlapping clusters), JK and BS are found to suffer from not being able to fit the specified model in many of the sub-samples formed. The PB also suffers when model fit is poor since it is reliant on data sets simulated from the model upon which to base the variance estimation calculations. However the WLBS will generally provide a robust solution, driven by the fact that all observations are represented with some weight in each of the sub-samples formed under this approach. [ABSTRACT FROM AUTHOR]

This article is concerned with standard errors (SE s) and confidence intervals (CIs) for exploratory factor analysis (EFA) in different situations. The authors adapt a sandwich SE estimator for EFA parameters to accommodate nonnormal data and imperfect models, factor extraction with maximum likelihood and ordinary least squares, and factor rotation with CF-varimax, CF-quartimax, geomin, or target rotation. They illustrate the sandwich SE s and CIs using nonnormal continuous data and ordinal data. They also compare SE estimates and CIs of the conventional information method, the sandwich method, and the bootstrap method using simulated data. The sandwich method and the bootstrap method are more satisfactory than the information method for EFA with nonnormal data and model approximation error. [ABSTRACT FROM AUTHOR]

In the presence of heteroskedasticity, conventional test statistics based on the ordinary least squares (OLS) estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of heteroskedasticity-consistent covariance matrices (HCCMs) have been developed in the literature. In contrast to the standard linear regression model, heteroskedasticity is a more serious problem for spatial econometric models, generally causing inconsistent extremum estimators of model coefficients. This paper investigates the finite sample properties of the heteroskedasticity-robust generalized method of moments estimator (RGMME) for a spatial econometric model with an unknown form of heteroskedasticity. In particular, it develops various HCCM-type corrections to improve the finite sample properties of the RGMME and the conventional Wald test. The Monte Carlo results indicate that the HCCM-type corrections can produce more accurate results for inference on model parameters and the impact effects estimates in small samples. [ABSTRACT FROM AUTHOR]

In experimental evaluations of policy interventions, the so-called Bloom adjustment is commonly used to estimate the impact of the treatment on the treated. It does so by rescaling the estimated impact of the intention to treat—that is, the overall treatment-control group difference in outcomes for the entire experimental sample—by the percentage of cases that took up the treatment offer. The practice of also rescaling the variance, as is common in the literature, imposes simplifying assumptions that may lead to biased variance estimates. We compare variances using the Bloom adjustment to variances that capture all the estimation error. While the difference between these variances is negligible in three experimental evaluations, we highlight three conditions that could result in larger relative bias: large impacts, large variability in compliance, and/or substantial endogeneity bias. The presence of these conditions could potentially result in a different conclusion for policy or practice. [ABSTRACT FROM AUTHOR]

In applications of structural equation modeling, the assumptions of correct model and normal data for maximum likelihood (ML) estimation almost never hold. Currently, the most common solution is to use a sandwich covariance estimator that is robust to (a) nonnormal data only (denoted as MLM), or (b) nonnormal data and incorrect model (denoted as MLR). In this paper, we propose new methods that perform even better than do MLM and MLR given model misfit, nonnormal data, and sample sizes typical of the behavioral sciences. In particular, we propose one standard error estimator and two confidence interval methods. The new methods also have analytic formulas and are thus light on computation burden. We first use statistical reasoning to show the new methods are superior to MLM and MLR, and then carry out simulations to compare ML, MLM, MLR, and the new methods. The simulation results confirm the superiority of the proposed methods. [ABSTRACT FROM AUTHOR]

Copyright of Industria Textila is the property of Institutul National de Cercetare-Dezvoltare pentru Textile si Pielarie and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The purpose of the article is to detect crop production on agricultural enterprises clusters at a price by defining their amplitude of price fluctuations. Methodology. The study is based on grouping of statistical data from agricultural enterprises using cluster analysis, followed by reliability evaluation of pre-selected clusters by t-test and charting the scope by the selected index. Cluster analysis of agricultural enterprises in Ukraine has been conducted using "STATISTICA" program. Distance between clusters was calculated as the Euclidean distance. The object of the study was data on the prices for agricultural enterprises by regions of Ukraine in 2013. As a result, an appropriate number of groups according to the produce types in the regions of Ukraine, plane in market prices, have been determined. The process of consistent combination of objects in clusters is shown in the graphs as agglomerative clustering dendrogram of the regions of Ukraine for such products as wheat, grain corn and sunflower seeds. In general, there have been examined: 7311 businesses growing wheat, 5034 – growing corn and 6124 companies growing sunflower. Results. During a year-long study of price fluctuations in agricultural enterprises within regions of Ukraine similarities in nature of absolute and relative changes in the formed clusters were established. Four clusters on wheat, five clusters on corn, three clusters on sunflower seeds have been allocated during the study. The study of the selected groups confirms significant differences between them and allows the sectors and enterprises of the cluster with high variability of prices to build their own marketing strategy based on the position of expectations and search for sale options according to the most favorable price. Practical value. The established differences on the selected clusters make it possible to forecast the price situation in various regions of Ukraine in terms of its differences from average by clusters for each product. Accordingly, it will enable specific producers to define the marketing strategy for pricing in the region for each product. Value/originality. The data on groups of growing crops permit to select forecast marketing strategies or rapid sale according to the prevailing prices.

The R package equateIRT implements item response theory (IRT) methods for equating different forms composed of dichotomous items. In particular, the IRT models included are the three-parameter logistic model, the two-parameter logistic model, the one-parameter logistic model and the Rasch model. Forms can be equated when they present common items (direct equating) or when they can be linked through a chain of forms that present common items in pairs (indirect or chain equating). When two forms can be equated through different paths, a single conversion can be obtained by averaging the equating coefficients. The package calculates direct and chain equating coefficients. The averaging of direct and chain coefficients that link the same two forms is performed through the bisector method. Furthermore, the package provides analytic standard errors of direct, chain and average equating coefficients.

Two new methods to estimate the asymptotic covariance matrix for marginal maximum likelihood estimation of cognitive diagnosis models (CDMs), the inverse of the observed information matrix and the sandwich‐type estimator, are introduced. Unlike several previous covariance matrix estimators, the new methods take into account both the item and structural parameters. The relationships between the observed information matrix, the empirical cross‐product information matrix, the sandwich‐type covariance matrix and the two approaches proposed by de la Torre (2009, J. Educ. Behav. Stat., 34, 115) are discussed. Simulation results show that, for a correctly specified CDM and Q‐matrix or with a slightly misspecified probability model, the observed information matrix and the sandwich‐type covariance matrix exhibit good performance with respect to providing consistent standard errors of item parameter estimates. However, with substantial model misspecification only the sandwich‐type covariance matrix exhibits robust performance. [ABSTRACT FROM AUTHOR]

The likelihood of a generalized linear mixed model (GLMM) often involves high-dimensional integrals, which in general cannot be computed explicitly. When direct computation is not available, method of simulated moments (MSM) is a fairly simpleway to estimate the parameters of interest. In this research, we compared parametric bootstrap (PB) and nonparametric bootstrapmethods (NPB) in estimating the standard errors of MSM estimators for GLMM. Simulation results show that when the group size is large, the PB and NPB perform similarly; when group size is medium, NPB performs better than PB in estimating standard errors of the mean. [ABSTRACT FROM AUTHOR]

Intellectual property right (IPR) has grown itself into one of the world's biggest and fastest-growing fields thereby necessitating the demand for critical analysis of scenarios associated with them for improving the need of mankind. Therefore, in the present study, an attempt is made to investigate two aspects related with intellectual properties (IP) at global level. Firstly, the significant difference of average number of IP filings and IP in-force between continents, when these IP are considered individually, and secondly, when these IP are studied together in the form of a single vector. [ABSTRACT FROM AUTHOR]

Estimating phylogenetic trees is an important problem in evolutionary biology, environmental policy, and medicine. Although trees are estimated, their uncertainties are generally discarded in statistical models for tree-valued data. Here, we explicitly model the multivariate uncertainty of tree estimates. We consider both the cases where uncertainty information arises extrinsically (through covariate information) and intrinsically (through the tree estimates themselves). The latter case is applicable to any procedure for tree estimation, and thus has broad relevance to the entire field of phylogenetics. The importance of accounting for tree uncertainty in tree space is demonstrated in two case studies. In the first instance, differences between gene trees are small relative to their uncertainties, while in the second, the differences are relatively large. Our main goal is visualization of tree uncertainty, and we demonstrate advantages of our method with respect to reproducibility, speed, and preservation of topological differences compared to visualization based on multidimensional scaling. The proposal highlights that phylogenetic trees are estimated in an extremely high-dimensional space, resulting in uncertainty information that cannot be discarded. Most importantly, it is a method that allows biologists to diagnose whether differences between gene trees are biologically meaningful or due to uncertainty in estimation. [ABSTRACT FROM AUTHOR]

When both model misspecifications and nonnormal data are present, it is unknown how trustworthy various point estimates, standard errors (SEs), and confidence intervals (CIs) are for standardized structural equation modeling parameters. We conducted simulations to evaluate maximum likelihood (ML), conventional robust SE estimator (MLM), Huber-White robust SE estimator (MLR), and the bootstrap (BS). We found (a) ML point estimates can sometimes be quite biased at finite sample sizes if misfit and nonnormality are serious; (b) ML and MLM generally give egregiously biased SEs and CIs regardless of the degree of misfit and nonnormality; (c) MLR and BS provide trustworthy SEs and CIs given medium misfit and nonnormality, but BS is better; and (d) given severe misfit and nonnormality, MLR tends to break down and BS begins to struggle. [ABSTRACT FROM AUTHOR]

Within the realm of logit-type random parameter models to address unobserved heterogeneity in preferences there are two dominant approaches: the mixed logit model, which assumes parametric and continuous heterogeneity distributions, and the latent class logit model, which is a discrete and semiparametric counterpart of mixed logit. In addition to offer flexibility benefits, random parameter models allow researchers to make conditional (posterior) inference on preference parameters at the individual-specific level. In this paper we extend the individual-specific experimental approach, that was conducted by Revelt and Train (2000) for the continuous heterogeneity distributions of a mixed logit, to the discrete case of the latent class logit model. Our Monte Carlo study results confirm the expectation that for a given number of individuals, the density of the conditional means converges to the conditional population as the number of choice situations increases. We also add to the analysis the behavior of interval estimates using two methods for the derivation of standard errors of the individual-specific estimates. In general, as we have more information of the choices made by the individuals, we are in better shape to identify individual-specific preferences. Our main conclusion is that accurate individual-specific estimation is possible – including correct assignment to classes, but a large number of choice situations is needed to correctly approximate the true underlying distribution. [ABSTRACT FROM AUTHOR]

In item response theory (IRT), when two groups from different populations take two separate tests, there is a need to link the two ability scales so that the item parameters of the tests are comparable across the groups. To link the two scales, information from common items are utilized to estimate linking coefficients which place the item parameters on the same scale. For polytomous IRT models, the Haebara and Stocking–Lord methods for estimating the linking coefficients have commonly been recommended. However, estimates of the variance for these methods are not available in the literature. In this article, the asymptotic variance of linking coefficients for polytomous IRT models with the Haebara and Stocking–Lord methods are derived. The results are presented in a general form and specific results are given for the generalized partial credit model. Simulations which investigate the accuracy of the derivations under various settings of model complexity and sample size are provided, showing that the derivations are accurate under the conditions considered and that the Haebara and Stocking–Lord methods have superior performance to several moment methods with performance close to that of concurrent calibration. [ABSTRACT FROM AUTHOR]

This paper develops formulae to compute the Fisher information matrix for the regression parameters of generalized linear models with Gaussian random effects. The Fisher information matrix relies on the estimation of the response variance under the model assumptions. We propose two approaches to estimate the response variance: the first is based on an analytic formula (or a Taylor expansion for cases where we cannot obtain the closed form), and the second is an empirical approximation using the model estimates via the expectation–maximization process. Further, simulations under several response distributions and a real data application involving a factorial experiment are presented and discussed. In terms of standard errors and coverage probabilities for model parameters, the proposed methods turn out to behave more reliably than does the ‘disparity rule’ or direct extraction of results from the generalized linear model fitted in the last expectation–maximization iteration. [ABSTRACT FROM AUTHOR]

The presence of textile dyes in the environment is a concern since they can be composed of organic and inorganic recalcitrant and/or toxic compounds. Ferromagnetic nanocomposites prepared from agroindustry residues, cork powder (CP), and yeast biomass (YB) were synthetized, characterized, and applied for dyes adsorption. The CP, YB, ferromagnetic nanoparticles (MNP), the ferromagnetic nanocomposites powdered cork (CP-MNP), and yeast biomass from ethanol industry (YB-MNP) were evaluated as sorbents for methylene blue. All sorbents were also applied to cleanup dyed cotton fabric effluent, containing a reactive dye trichomy (Yellow CL-2R, Red CL-5B, and Blue HF-RL, all Drimaren). The experimental sorption capacities (SCexp) of the sorbents for methylene blue with the respective confidence intervals (1 –α = 0.95) were 36.4 ± 0.5 mg/g (CP), 33.6 ± 0.9 mg/g (CP-MNP), 27.7 ± 0.2 mg/g (YB), 30.4 ± 1.2 mg/g (YB-MNP), and 2.9 ± 0.5 mg/g (MNP). A proposed decision algorithm was employed to define the isotherms model best fit to each process, and we found CP-MNP (Langmuir), CP (Langmuir), YB-MNP (SIPS), YB (SIPS), and MNP (Freundlich). High sorption efficiency reached up recoveries of 50.0% ± 1.2% (CP), 66.5% ± 0.1% (CP-MNP), 85.9% ± 0.1% (YB), and 82.6% ± 0.1% (YB-MNP) of dye effluents, suggesting that the materials present potential for treatment effluents. [ABSTRACT FROM AUTHOR]

The Expectation-Maximization (EM) algorithm is a method for finding the maximum likelihood estimate of a model in the presence of missing data. Unfortunately, EM does not produce a parameter covariance matrix for standard errors. Both Oakes and Supplemented EM are methods for obtaining the parameter covariance matrix. SEM was discovered in 1991 and is implemented in both open-source and commercial item response model estimation software. Oakes, a more recent method discovered in 1999, had not been implemented in item response model software until now. Convergence properties, accuracy, and elapsed time of Oakes and Supplemental EM family algorithms are compared for a diverse selection IFA models. Oakes exhibits the best accuracy and elapsed time among algorithms compared. We recommend that Oakes be made available in item response model estimation software.