root n consistent semiparametric regression on considere une variable aleatoire x z dans 7b r p 7b r q on construit un estimateur generalisant l estimateur des moindres carres ordinaires en inserant des estimateurs non parametriques de la regression dans la projection orthogonale non lineaire sur z

semiparametric estimation of censored selection models with a nonparametric selection mechanism abstract in this paper estimation of the coefficients in a single index selectivity bias model is considered under the assumption that the selection correction function depends on the conditional mean of some observable selection variable the estimation method follows a familiar two step strategy the first step uses a nonparametric regression estimator for the selection variable while the second step uses a weighted instrumental variables estimator for the coefficients in the equation of interest the paper gives conditions under which the proposed estimator is root n consistent and asymptotically normal the proposed method is applied to data on labor supply

factors affecting the use of noise band vocoders as acoustic models for pitch perception in cochlear implants although in a number of experiments noise band vocoders have been shown to provide acoustic models for speech perception in cochlear implants ci the present study assesses in four experiments whether and under what limitations noise band vocoders can be used as an acoustic model for pitch perception in ci the first two experiments examine the effect of spectral smearing on simulated electrode discrimination and fundamental frequency f0 discrimination the third experiment assesses the effect of spectral mismatch in an f0 discrimination task with two different vocoders the fourth experiment investigates the effect of amplitude compression on modulation rate discrimination for each experiment the results obtained from normal hearing subjects presented with vocoded stimuli are compared to results obtained directly from ci recipients the results show that place pitch sensitivity drops with increased spectral smearing and that place pitch cues for multi channel stimuli can adequately be mimicked when

finite mixture distributions sequential likelihood and the em algorithm a popular way to account for unobserved heterogeneity is to assume that the data are drawn from a finite mixture distribution a barrier to using finite mixture models is that parameters that could previously be estimated in stages must now be estimated jointly using mixture distributions destroys any additive separability of the log likelihood function we show however that an extension of the em algorithm reintroduces additive separability thus allowing one to estimate parameters sequentially during each maximization step in establishing this result we develop a broad class of estimators for mixture models returning to the likelihood problem we show that relative to full information maximum likelihood our sequential estimator can generate large computational savings with little loss of efficiency

discrete factor approximations in simultaneous equation models estimating the impact of a dummy endogenous variable on a continuous outcome this paper contains a monte carlo evaluation of estimators used to control for endogeneity of dummy explanatory variables in continuous outcome regression models when the true model has bivariate normal disturbances estimators using discrete factor approximations compare favorably to efficient estimators in terms of precision and bias these approximation estimators dominate all the other estimators examined when the disturbances are non normal the experiments also indicate that one should liberally add points of support to the discrete factor distribution the paper concludes with an application of the discrete factor approximation to the estimation of the impact of marriage on wages

tests of linear hypotheses and l 1 estimation statistics of a linear hypothesis in the standard linear model these test statistics which correspond to wald likelihood ratio and lagrange multiplier tests are shown to have the same limiting chi square behavior under mild regularity conditions on design and the distribution of errors the asymptotic theory of the tests is derived for a large class of error distributions thus in huber s 10 terminology we investigate the behavior of the likelihood ratio test under non standard conditions the asymptotic efficiency of the 11 tests involves a modest sacrifice of power compared to classical tests in cases of strictly gaussian errors but may yield large efficiency gains in non gaussian situations the lagrange multiplier test seems particularly attractive from a computational standpoint we derive the asymptotic distribution of the three alternative 11 test statistics for a simple linear exclusion hypothesis extension of these results to hypotheses of the form r 8 r is a straightforward exercise when the density of the error distribution is strictly positive at the median all three test statistics have the same limiting central x2 behavior at the null and noncentral x2 behavior for local alternatives to the null when the variance of the error distribution is bounded analogous results are well known for classical forms of the wald likelihood ratio and lagrange multipler tests based on least squares methods see for example silvey 18 and the discussion in section 4 below

on the asymptotic properties of estimators of models containing limited dependent variables for the tobit model with independent observations amemiya 1 has established the strong consistency and asymptotic normality of a stationary point 9 of the log likelihood the likelihood for dependent observations may be computationally intractable so the behavior of 9 in the presence of serially correlated observations is of interest under a relaxation of amemiya s assumption of independence we prove that 9 is strongly consistent and asymptotically normal and give an expression for the limiting covariance matrix

distribution free maximum likelihood estimator of the binary choice model1 is a given function of the exogenous variables z and unknown parameters 9 representing the systematic component of the utility difference and f is the distribution function of the random component of the utility difference this paper describes a method of estimating the parameters 9 without assuming any functional form for the distribution function f and proves that this estimator is consistent f is also consistently estimated the method uses maximum likelihood estimation in which the likelihood is maximized not only over the parameter 9 but also over a space which contains all distribution functions

time and causality a monte carlo assessment of the timing of events approach we present new monte carlo evidence regarding the feasibility of separating causality from selection within non experimental duration data by means of the non parametric maximum likelihood estimator npmle key findings are i the npmle is extremely reliable and it accurately separates the causal effects of treatment and duration dependence from sorting effects almost regardless of the true unobserved heterogeneity distribution ii the npmle is normally distributed and standard errors can be computed directly from the optimally selected model and iii unjustified restrictions on the heterogeneity distribution e g in terms of a pre specified number of support points may cause substantial bias

perception of the envelope beat frequency of inharmonic complex temporal envelopes listeners can hear slow sinusoidal variations in the depth of sinusoidally amplitude modulated sam stimuli here the sam stimulus of frequency fm acts as the carrier and the slow variation in depth of frequency fm referred to as second order amplitude modulation corresponds to a beat in the temporal envelope recent studies have suggested that second order amplitude modulation perception is based on a modulation distortion component or the venelope the hilbert envelope of the ac coupled hilbert envelope both occurring at the envelope beat frequency fm this was tested by transposing to the modulation domain the matching paradigm used by schouten et al j acoust soc am 34 1418 1424 1962 listeners estimated the envelope beat frequency evoked by a 5 hz second order sam white noise with fm either an integer multiple of fm or shifted in frequency to make the complex envelope inharmonic the results indicate that the perception of the envelope beat frequency was affected by these shif

discrimination and identification of modulation rate using a noise carrier modulation rate perception was measured for three tasks a fixed standard forced choice discrimination task with a 500 ms interstimulus interval a random standard forced choice discrimination task with an 8 s interstimulus interval and an identification task thresholds were obtained for modulation rates from 14 224 hz using noise carriers bandpass filtered from 500 4000 hz 500 1600 hz 1700 2800 hz and 2900 4000 hz the four bands yielded similar results except for modulation rates greater than 100 hz where the 500 to 1600 hz thresholds were higher fixed standard discrimination thresholds were about 3 hz for modulation rates up to 66 hz the increase of thresholds for modulation rates above 66 hz could be due to temporal resolution limits with a time constant of about 2 3 ms for modulation rates above 100 hz critical band filtering may further decrease sensitivity for the 500 to 1600 hz noise band resolution in the random standard discrimination task was similar to that for the identificatio

the consistency of nonlinear regression minimizing the l_1 norm we consider conditions in a nonlinear regression model for the consistency of the estimator obtained by minimizing the l 1 norm i e the sum of absolute deviations

consistent estimation of limited dependent variable models despite misspecification of distribution abstract by exploiting conditions on the behavior of regressors estimators are developed which are consistent up to a factor of proportionality for the slope coefficients of the regression function of a imperfectly observed dependent variable limited dependent variables are a special case of interest the consistency is robust to misspecification of the distribution of the latent dependent variable the estimators are weighted pseudo maximum likelihood estimators which can be readily computed using popular methods and software

least absolute deviations estimation for the censored regression model this paper proposes an alternative to maximum likelihood estimation of the parameters of the censored regression or censored tobit model the proposed estimator is a generalization of least absolute deviations estimation for the standard linear model and unlike estimation methods based on the assumption of normally distributed error terms the estimator is consistent and asymptotically normal for a wide class of error distributions and is also robust to heteroscedasticity the paper gives the regularity conditions and proofs of these large sample results and proposes classes of consistent estimators of the asymptotic covariance matrix for both homoscedastic and heteroscedastic disturbances

abstract cauchy problems for quasilinear operators whose domains are not necessarily dense or constant abstract the solvability of the abstract cauchy problem for the quasilinear evolution equation u t a u t u t for t 0 and u 0 u 0 d is discussed here a w w y is a family of closed linear operators in a real banach space x such that y d a w y for w y y is another banach space which is continuously embedded in x and d is a closed subset of y the existence and uniqueness of c 1 solutions to the cauchy problem is proved without assuming that y is dense in x or d a w is independent of w the abstract result is applied to obtain an l 1 valued c 1 solution to a size structured population model

a general instrumental variable framework for regression analysis with outcome missing not at random summary the instrumental variable iv design is a well known approach for unbiased evaluation of causal effects in the presence of unobserved confounding in this article we study the iv approach to account for selection bias in regression analysis with outcome missing not at random in such a setting a valid iv is a variable which i predicts the nonresponse process and ii is independent of the outcome in the underlying population we show that under the additional assumption iii that the iv is independent of the magnitude of selection bias due to nonresponse the population regression in view is nonparametrically identified for point estimation under i iii we propose a simple complete case analysis which modifies the regression of primary interest by carefully incorporating the iv to account for selection bias the approach is developed for the identity log and logit link functions for inferences about the marginal mean of a binary outcome assuming i and ii only we describe novel and approximately sharp bounds which unlike robins manski bounds are smooth in model parameters therefore allowing for a straightforward approach to account for uncertainty due to sampling variability these bounds provide a more honest account of uncertainty and allows one to assess the extent to which a violation of the key identifying condition iii might affect inferences for illustration the methods are used to account for selection bias induced by hiv testing nonparticipation in the evaluation of hiv prevalence in the zambian demographic and health surveys

local controllability of quasilinear integrodifferential evolution systems in banach spaces sufficient conditions for local controllability of quasilinear integrodifferential systems in banach spaces are established the results are obtained by using the analytic semigroup theory and the schauder fixed point theorem an example is provided to illustrate the theory

two stage least absolute deviations estimators1 in this paper the method of least absolute deviations is applied to the estimation of the parameters of a structural equation in the simultaneous equations model a class of estimators called two stage least absolute deviations estimators is defined their asymptotic properties are derived and the problem of finding the optimal member of the class is considered in this paper we apply the method of least absolute deviations to the estimation of the parameters of a structural equation in the simultaneous equations model we define a class of estimators called two stage least absolute deviations estimators 2slad and derive their asymptotic properties they are so named as their relationship to the two stage least squares estimator 2sls is analogous to the relationship of the least absolute deviations estimator lad to the least squares estimator ls in the standard regression model the lad estimation has been extensively studied in the context of the standard regression model and its usefulness is universally recognized in this paper we show that the advantage of 2slad over 2sls in the simultaneous equations model can be as great as that of lad over ls in the standard regression model if 2slad is properly defined this last clause is very important since the results of this paper indicate that the lad analogue of 2sls that has been considered before in the literature is not an appropriate method

bounded influence estimators for the tobit model abstract this paper introduces a class of bounded influence estimators for the gaussian censored regression or tobit model these estimators can be interpreted as weighted ml estimators with weights chosen to attain the best trade off between efficiency and robustness an empirical example illustrates the feasibility and usefulness of these estimators as well as their performance vis a vis the tobit ml clad and scls estimators

two step estimation of heteroskedastic sample selection models abstract this paper considers two step estimation of a sample selection model in which there is heteroskedasticity of unknown form in the latent errors we propose an estimator which uses recent developments in nonparametric regression estimation involving series approximations the estimator is shown to be consistent and asymptotically normally distributed under reasonable conditions a small monte carlo experiment demonstrates the usefulness of the estimator and highlights the bias inherent in the usual heckman 1979 estimator when there is heteroskedasticity

estimation of a censored regression panel data model using conditional moment restrictions efficiently honore has introduced a semiparametric estimator for the censored regression panel data model with fixed individual effects newey has shown how to obtain efficient estimators under a given conditional moment restriction we apply newey s approach to obtain a two step gmm estimator which is more efficient than the honore estimator we compare this estimator to the honore estimator and to parametric estimators including chamberlain s quasi fixed effects estimator in a monte carlo experiment we also extend the honore estimator and the two step gmm estimator to the case of a balanced or unbalanced panel of more than two waves we apply the estimators to an empirical example concerning earnings of married females using data from the dutch socio economic panel

further evidence on the robustness of the tobit estimator to heteroskedasticity abstract when the error terms in a tobit model are heteroskedastic the mle which assumes homoskedasticity is inconsistent for the special case of a constant term only model we investigate the size of the inconsistency the inconsistency is greater the greater the heteroskedasticity and the greater the degree of censoring i e the greater the number of limit observations however the inconsistency is much smaller than in the corresponding truncated normal model considered by hurd