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Regression Estimation (regression + estimation)
Selected AbstractsNonlinear regression checking via local polynomial smoothing with applications to thermogravimetric analysisJOURNAL OF CHEMOMETRICS, Issue 6 2009Ricardo Cao Abstract A goodness-of-fit test statistic for nonlinear regression models based on local polynomial estimation is proposed in this paper. The criterion used to construct the test is the distance between the parametric fit and the nonparametric regression estimation. The good performance of the test is shown via a simulation study. The method is applied to check a logistic mixture regression model for real data coming from a thermal analysis problem. Copyright © 2009 John Wiley & Sons, Ltd. [source] Noninvasive assessment of energy expenditure in childrenAMERICAN JOURNAL OF HUMAN BIOLOGY, Issue 5 2006Isabelle Sarton-Miller This study establishes an affordable, simple, and noninvasive method to assess energy expenditure (EE) in children, an underrepresented group. The method is based on regression modeling, where prediction of oxygen consumption (VO2), a proxy of EE, was deduced from heart rate (HR) and several variables that adjusted for interindividual variability. Limb activities (arms vs. legs) and posture (sitting vs. standing) were represented in the regression as dichotomous covariates. The order of activities and intensities was randomized. Seventy-four children (aged 7,10 years), raised at sea-level (Seattle, WA), comprised the sample. Anthropometric measures were taken, and VO2 and HR were measured for activities using the arms in sitting and standing positions (mixing and punching), as well as walking at different velocities on a treadmill. Repeated measures and least square regression estimation were used. HR, body mass, number of hours of physical activity per week (HPA), an interaction term between sitting and standing resting HR, and the two dichotomous variables, sex and limbs, were significant covariates; posture was not. Several equations were developed for various field uses. The equations were built from sea-level data, but ultimately this method could serve as a baseline for developing a similar approach in other populations, where noninvasive estimation of EE is imperative in order to gain a better understanding of children's energetic issues. Am. J. Hum. Biol. 18:600,609, 2006. © 2006 Wiley-Liss, Inc. [source] Submarket Dynamics of Time to SaleREAL ESTATE ECONOMICS, Issue 3 2006Gwilym Pryce We argue that the rush to apply multiple regression estimation to time on the market (TOM) durations may have led to important details and idiosyncrasies in local housing market dynamics being overlooked. What is needed is a more careful examination of the fundamental properties of time to sale data. The approach promoted and presented here, therefore, is to provide an examination of housing sale dynamics using a step-by-step approach. We present three hypotheses about TOM: (i) there is nonmonotonic duration dependence in the hazard of sale, (ii) the hazard curve will vary both over time and across intra-urban areas providing evidence of the existence of submarkets and (iii) institutional idiosyncrasies can have a profound effect on the shape and position of the hazard curve. We apply life tables, kernel-smoothed hazard functions and likelihood ratio tests for homogeneity to a large Scottish data set to investigate these hypotheses. Our findings have important implications for TOM analysis. [source] Nonparametric two-step regression estimation when regressors and error are dependentTHE CANADIAN JOURNAL OF STATISTICS, Issue 2 2000Jons Pinkse Abstract This paper considers estimation of the function g in the model Yt = g(Xt ) + ,t when E(,t|Xt) , 0 with nonzero probability. We assume the existence of an instrumental variable Zt that is independent of ,t, and of an innovation ,t = Xt , E(Xt|Zt). We use a nonparametric regression of Xt on Zt to obtain residuals ,t, which in turn are used to obtain a consistent estimator of g. The estimator was first analyzed by Newey, Powell & Vella (1999) under the assumption that the observations are independent and identically distributed. Here we derive a sample mean-squared-error convergence result for independent identically distributed observations as well as a uniform-convergence result under time-series dependence. Cet article concerne l'estimation de la fonction g dans le modèle Yt = g(Xt) + ,t où E(,t| Xt) , 0 avec probabilité non nulle. Les auteurs supposent l'existence d'une 'variable instrumentale' Zt qui est indépendante de ,t et de l'innovation ,t = Xt , E(Xt|Zt). Les résidus ,t déduits d'une régression non paramétrique de Xt sur Zt permettent d'obtenir une estimation convergente de g. Cette façon de procéder avait déjà été proposée par Newey, Powell & Vella (1999) dans le cas où les observations for-ment un échantillon aléatoire. Les auteurs démontrent ici la convergence de 1'erreur quadratique moyenne expérimentale sous les m,mes conditions et établissent un résultat de convergence uniforme sous des conditions de dépendance sérielle entre les observations. [source] VARIANCE ESTIMATION IN TWO-PHASE SAMPLINGAUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS, Issue 2 2009M.A. Hidiroglou Summary Two-phase sampling is often used for estimating a population total or mean when the cost per unit of collecting auxiliary variables, x, is much smaller than the cost per unit of measuring a characteristic of interest, y. In the first phase, a large sample s1 is drawn according to a specific sampling design p(s1), and auxiliary data x are observed for the units i,s1. Given the first-phase sample s1, a second-phase sample s2 is selected from s1 according to a specified sampling design {p(s2,s1) }, and (y, x) is observed for the units i,s2. In some cases, the population totals of some components of x may also be known. Two-phase sampling is used for stratification at the second phase or both phases and for regression estimation. Horvitz,Thompson-type variance estimators are used for variance estimation. However, the Horvitz,Thompson (Horvitz & Thompson, J. Amer. Statist. Assoc. 1952) variance estimator in uni-phase sampling is known to be highly unstable and may take negative values when the units are selected with unequal probabilities. On the other hand, the Sen,Yates,Grundy variance estimator is relatively stable and non-negative for several unequal probability sampling designs with fixed sample sizes. In this paper, we extend the Sen,Yates,Grundy (Sen, J. Ind. Soc. Agric. Statist. 1953; Yates & Grundy, J. Roy. Statist. Soc. Ser. B 1953) variance estimator to two-phase sampling, assuming fixed first-phase sample size and fixed second-phase sample size given the first-phase sample. We apply the new variance estimators to two-phase sampling designs with stratification at the second phase or both phases. We also develop Sen,Yates,Grundy-type variance estimators of the two-phase regression estimators that make use of the first-phase auxiliary data and known population totals of some of the auxiliary variables. [source] Factors Associated with the Income Distribution of Full-Time Physicians: A Quantile Regression ApproachHEALTH SERVICES RESEARCH, Issue 5 2007Ya-Chen Tina Shih Objective. Physician income is generally high, but quite variable; hence, physicians have divergent perspectives regarding health policy initiatives and market reforms that could affect their incomes. We investigated factors underlying the distribution of income within the physician population. Data Sources. Full-time physicians (N=10,777) from the restricted version of the 1996,1997 Community Tracking Study Physician Survey (CTS-PS), 1996 Area Resource File, and 1996 health maintenance organization penetration data. Study Design. We conducted separate analyses for primary care physicians (PCPs) and specialists. We employed least square and quantile regression models to examine factors associated with physician incomes at the mean and at various points of the income distribution, respectively. We accounted for the complex survey design for the CTS-PS data using appropriate weighted procedures and explored endogeneity using an instrumental variables method. Principal Findings. We detected widespread and subtle effects of many variables on physician incomes at different points (10th, 25th, 75th, and 90th percentiles) in the distribution that were undetected when employing regression estimations focusing on only the means or medians. Our findings show that the effects of managed care penetration are demonstrable at the mean of specialist incomes, but are more pronounced at higher levels. Conversely, a gender gap in earnings occurs at all levels of income of both PCPs and specialists, but is more pronounced at lower income levels. Conclusions. The quantile regression technique offers an analytical tool to evaluate policy effects beyond the means. A longitudinal application of this approach may enable health policy makers to identify winners and losers among segments of the physician workforce and assess how market dynamics and health policy initiatives affect the overall physician income distribution over various time intervals. [source] | ||||||||||