Likelihood-based estimation of the regression model with scrambled responses

A significant problem in the collection of responses to potentially sensitive questions, such as relating to illegal, immoral or embarrassing activities, is non-sampling error due to refusal to respond or false responses. Eichhorn & Hayre (1983) suggested the use of scrambled responses to reduce this form of bias. This paper considers a linear regression model in which the dependent variable is unobserved but for which the sum or product with a scrambling random variable of known distribution, is known. The performance of two likelihood-based estimators is investigated, namely of a Bayesian estimator achieved through a Markov chain Monte Carlo (MCMC) sampling scheme, and a classical maximum-likelihood estimator. These two estimators and an estimator suggested by Singh, Joarder & King (1996) are compared. Monte Carlo results show that the Bayesian estimator outperforms the classical estimators in almost all cases, and the relative performance of the Bayesian estimator improves as the responses become more scrambled.

Estimating correlations from epidemiological data in the presence of measurement error

Analysis of a major multi-site epidemiologic study of heart disease has required estimation of the pairwise correlation of several measurements across sub-populations. Because the measurements from each sub-population were subject to sampling variability, the Pearson product moment estimator of these correlations produces biased estimates. This paper proposes a model that takes into account within and between sub-population variation, provides algorithms for obtaining maximum likelihood estimates of these correlations and discusses several approaches for obtaining interval estimates. (C) 1997 by John Wiley & Sons, Ltd.

Food energy content influences food portion size estimation by nutrition students

Background: Food portion size estimation involves a complex mental process that may influence food consumption evaluation. Knowing the variables that influence this process can improve the accuracy of dietary assessment. The present study aimed to evaluate the ability of nutrition students to estimate food portions in usual meals and relate food energy content with errors in food portion size estimation. Methods: Seventy-eight nutrition students, who had already studied food energy content, participated in this cross-sectional study on the estimation of food portions, organised into four meals. The participants estimated the quantity of each food, in grams or millilitres, with the food in view. Estimation errors were quantified, and their magnitude were evaluated. Estimated quantities (EQ) lower than 90% and higher than 110% of the weighed quantity (WQ) were considered to represent underestimation and overestimation, respectively. Correlation between food energy content and error on estimation was analysed by the Spearman correlation, and comparison between the mean EQ and WQ was accomplished by means of the Wilcoxon signed rank test (P < 0.05). Results: A low percentage of estimates (18.5%) were considered accurate (+/- 10% of the actual weight). The most frequently underestimated food items were cauliflower, lettuce, apple and papaya; the most often overestimated items were milk, margarine and sugar. A significant positive correlation between food energy density and estimation was found (r = 0.8166; P = 0.0002). Conclusions: The results obtained in the present study revealed a low percentage of acceptable estimations of food portion size by nutrition students, with trends toward overestimation of high-energy food items and underestimation of low-energy items.

Sonographic estimation of fetal weight in macrosomic fetuses: diabetic versus non-diabetic pregnancies

The objective of this study is to compare the accuracy of sonographic estimation of fetal weight of macrosomic babies in diabetic vs non-diabetic pregnancies. Ali babies weighing 4000 g or more at birth, and who had ultrasound scans performed within one week of delivery were included in this retrospective study. Pregnancies with diabetes mellitus were compared to those without diabetes mellitus. The mean simple error (actual birthweight - estimated fetal weight); mean standardised absolute error (absolute value of simple error (g)/actual birthweight (kg)); and the percentage of estimated birthweight falling within 15% of the actual birthweight between the two groups were compared. There were 9516 deliveries during the study period. Of this total 1211 (12.7 %) babies weighed 4000 g or more. A total of 56 non-diabetic pregnancies and 19 diabetic pregnancies were compared. The average sonographic estimation of fetal weight in diabetic pregnancies was 8 % less than the actual birthweight, compared to 0.2 % in the non-diabetic group (p < 0.01). The estimated fetal weight was within 15% of the birthweight in 74 % of the diabetic pregnancies, compared to 93 % of the non-diabetic pregnancies (p < 0.05). In the diabetic group, 26.3 % of the birthweights were underestimated by more than 15 %, compared to 5.4 % in the non-diabetic group (p < 0.05). In conclusion, the prediction accuracy of fetal weight estimation using standard formulae in macrosomic fetuses is significantly worse in diabetic pregnancies compared to non-diabetic pregnancies. When sonographic fetal weight estimation is used to influence the mode of delivery for diabetic women, a more conservative cut-off needs to be considered.

On the development of an automatic voice pleasantness classification and intensity estimation system

In the last few years, the number of systems and devices that use voice based interaction has grown significantly. For a continued use of these systems, the interface must be reliable and pleasant in order to provide an optimal user experience. However there are currently very few studies that try to evaluate how pleasant is a voice from a perceptual point of view when the final application is a speech based interface. In this paper we present an objective definition for voice pleasantness based on the composition of a representative feature subset and a new automatic voice pleasantness classification and intensity estimation system. Our study is based on a database composed by European Portuguese female voices but the methodology can be extended to male voices or to other languages. In the objective performance evaluation the system achieved a 9.1% error rate for voice pleasantness classification and a 15.7% error rate for voice pleasantness intensity estimation.

Traveled Distance Estimation Algorithm for Indoor Localization

This paper presents an ankle mounted Inertial Navigation System (INS) used to estimate the distance traveled by a pedestrian. This distance is estimated by the number of steps given by the user. The proposed method is based on force sensors to enhance the results obtained from an INS. Experimental results have shown that, depending on the step frequency, the traveled distance error varies between 2.7% and 5.6%.

Estimation of signal subspace on hyperspectral data

Dimensionality reduction plays a crucial role in many hyperspectral data processing and analysis algorithms. This paper proposes a new mean squared error based approach to determine the signal subspace in hyperspectral imagery. The method first estimates the signal and noise correlations matrices, then it selects the subset of eigenvalues that best represents the signal subspace in the least square sense. The effectiveness of the proposed method is illustrated using simulated and real hyperspectral images.

Hyperspectral signal subspace estimation

Given an hyperspectral image, the determination of the number of endmembers and the subspace where they live without any prior knowledge is crucial to the success of hyperspectral image analysis. This paper introduces a new minimum mean squared error based approach to infer the signal subspace in hyperspectral imagery. The method, termed hyperspectral signal identification by minimum error (HySime), is eigendecomposition based and it does not depend on any tuning parameters. It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.

Relationship between estimation and real motor performance in school-age children

The relationship between estimated and real motor competences was analyzed for several tasks. Participants were 303 children (160 boys and 143 girls), which had between 6 and 10 years of age (M=8.63, SD=1.16). None of the children presented developmental difficulties or learning disabilities, and all attended age-appropriate classes. Children were divided into three groups according to their age: group 1 (N= 102; age range: 6.48-8.01 years); group 2 (N= 101; age range: 8.02-9.22 years); and group 3 (N=100; age range: 9.24-10.93 years). Children were asked to predict their maximum distance for a locomotor, a manipulative, and a balance task, prior to performing those tasks. Children’s estimations were compared with their real performance to determine their accuracy. Children had, in general, a tendency to overestimate their performance (standing long jump: 56.11%, kicking: 63.37%, throwing: 73.60%, and Walking Backwards (WB) on a balance beam: 45.21%), and older children tended to be more accurate, except for the manipulative tasks. Furthermore, the relationship between estimation and real performance in children with different levels of motor coordination (Köperkoordinationstest für Kinder, KTK) was analyzed. The 75 children with the highest score comprised the Highest Motor Coordination (HMC) group, and the 78 children with the lowest score were placed in the Lowest Motor Coordination (LMC) group. There was a tendency for LMC and HMC children to overestimate their skills at all tasks, except for the HMC group at the WB task. Children with the HMC level tended to be more accurate when predicting their motor performance; however, differences in absolute percent error were only significant for the throwing and WB tasks. In conclusion, children display a tendency to overestimate their performance independently of their motor coordination level and task. This fact may be determinant to the development of their motor competences, since they are more likely to engage and persist in motor tasks, but it might also increase the occurrence of unintended injuries.

A comparative analysis of Cobb-Douglas model with additive and multiplicative error

This paper dis cusses the fitting of a Cobb-Doug las response curve Yi = αXβi, with additive error, Yi = αXβi + e i, instead of the usual multiplicative error Yi = αXβi (1 + e i). The estimation of the parameters A and B is discussed. An example is given with use of both types of error.

Parametric and semiparametric estimation of sample selection models: an empirical application to the female labour force in Portugal

This comment corrects the errors in the estimation process that appear in Martins (2001). The first error is in the parametric probit estimation, as the previously presented results do not maximize the log-likelihood function. In the global maximum more variables become significant. As for the semiparametric estimation method, the kernel function used in Martins (2001) can take on both positive and negative values, which implies that the participation probability estimates may be outside the interval [0,1]. We have solved the problem by applying local smoothing in the kernel estimation, as suggested by Klein and Spady (1993).

Estimation of the Spatial Weights Matrix under Structural Constraints

While estimates of models with spatial interaction are very sensitive to the choice of spatial weights, considerable uncertainty surrounds de nition of spatial weights in most studies with cross-section dependence. We show that, in the spatial error model the spatial weights matrix is only partially identi ed, and is fully identifi ed under the structural constraint of symmetry. For the spatial error model, we propose a new methodology for estimation of spatial weights under the assumption of symmetric spatial weights, with extensions to other important spatial models. The methodology is applied to regional housing markets in the UK, providing an estimated spatial weights matrix that generates several new hypotheses about the economic and socio-cultural drivers of spatial di¤usion in housing demand.

Estimation of lean meat content in pig carcasses using X-ray Computed Tomography and PLS regression

Lean meat percentage (LMP) is an important carcass quality parameter. The aim of this work is to obtain a calibration equation for the Computed Tomography (CT) scans with the Partial Least Square Regression (PLS) technique in order to predict the LMP of the carcass and the different cuts and to study and compare two different methodologies of the selection of the variables (Variable Importance for Projection — VIP- and Stepwise) to be included in the prediction equation. The error of prediction with cross-validation (RMSEPCV) of the LMP obtained with PLS and selection based on VIP value was 0.82% and for stepwise selection it was 0.83%. The prediction of the LMP scanning only the ham had a RMSEPCV of 0.97% and if the ham and the loin were scanned the RMSEPCV was 0.90%. Results indicate that for CT data both VIP and stepwise selection are good methods. Moreover the scanning of only the ham allowed us to obtain a good prediction of the LMP of the whole carcass.

Nonclassical measurement error with applications to labor and development issues

Zero correlation between measurement error and model error has been assumed in existing panel data models dealing specifically with measurement error. We extend this literature and propose a simple model where one regressor is mismeasured, allowing the measurement error to correlate with model error. Zero correlation between measurement error and model error is a special case in our model where correlated measurement error equals zero. We ask two research questions. First, we wonder if the correlated measurement error can be identified in the context of panel data. Second, we wonder if classical instrumental variables in panel data need to be adjusted when correlation between measurement error and model error cannot be ignored. Under some regularity conditions the answer is yes to both questions. We then propose a two-step estimation corresponding to the two questions. The first step estimates correlated measurement error from a reverse regression; and the second step estimates usual coefficients of interest using adjusted instruments.

Simultaneous estimation of indirect and interaction effects using structural equation models

Interaction effects are usually modeled by means of moderated regression analysis. Structural equation models with non-linear constraints make it possible to estimate interaction effects while correcting formeasurement error. From the various specifications, Jöreskog and Yang's(1996, 1998), likely the most parsimonious, has been chosen and further simplified. Up to now, only direct effects have been specified, thus wasting much of the capability of the structural equation approach. This paper presents and discusses an extension of Jöreskog and Yang's specification that can handle direct, indirect and interaction effects simultaneously. The model is illustrated by a study of the effects of an interactive style of use of budgets on both company innovation and performance