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3 edition of Interval estimation of potentially misspecified quantile models in the presence of missing data found in the catalog.

Interval estimation of potentially misspecified quantile models in the presence of missing data

Patrick Kline

Interval estimation of potentially misspecified quantile models in the presence of missing data

by Patrick Kline

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Published by National Bureau of Economic Research in Cambridge, MA .
Written in English


Edition Notes

StatementPatrick Kline, Andres Santos.
SeriesNBER working paper series -- working paper 15716, Working paper series (National Bureau of Economic Research : Online) -- working paper no. 15716.
ContributionsSantos, Andrés., National Bureau of Economic Research.
Classifications
LC ClassificationsHB1
The Physical Object
FormatElectronic resource
ID Numbers
Open LibraryOL24101582M
LC Control Number2010655732

  We estimate the model for all the deciles and we impose the fixed effects to be independent of the quantile of interest, τ, by estimating a weighted QR model (using the same weights for all quantiles). We use data from the Compustat Industrial Annual dataset. The sample consists of annual CRSP/Compustat data from the years through Performance in coverage probability of prediction intervals. It is often of interest to evaluate the accuracy of quantile regression in offering the prediction interval of Y given x T β this end, we can estimate the τ-th and (1 − τ)-th quantile of the conditional distribution of Y given x T β 0, which can be used as the confidence interval at the level of (1 − 2τ) for τ.

Quantile Regression is a wa y to model different quantiles of the dependent variable as a function of cov ariates (see Koenker and Bassett Jr ; Koenker ). Given the. interval. So the estimated conditional probability of sur-viving the interval is 1 d=r; (4) Tied deaths and censoring - assume censorings last to the end of the interval, so that the estimated condi-tional probability of surviving the interval is still 1 d=r. General Formula for jth interval.

In fact the quantile regression line acts as a “moving threshold” in such a way that on average (in the case of P75) a quarter of the data lies above it. Nevertheless, thresholding an logistic regression could be an interesting venue for longitudinal data modelling, because mixed model technology for binary responses is available. Quantiles and percentiles. Quantiles are defined by ordering data into q equally sized data subsets and noting the boundaries. The kth q-quantile for a random variable X is the value x such that the probability that the random variable will be less than x is at most k / q and the probability that the random variable will be more than x is at most (q − k) / q.


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Interval estimation of potentially misspecified quantile models in the presence of missing data by Patrick Kline Download PDF EPUB FB2

We thank Ivan Fernández‐Val for assistance in replicating the results of Angrist, Chernozhukov, and Fernández‐Val (). A previous version of this paper circulated under the title “Interval estimation of potentially misspecified quantile models in the presence of missing data.” Search for more papers by this authorCited by: estimating models of conditional quantiles with missing outcome data and discrete covariates.

We restrict the degree of non-ignorable selection governing the missingness process by imposing bounds on the Kolmogorov-Smirnov (KS) distance between the distribution of outcomes among missing observations and the overall (unselected) distribution.

Get this from a library. Sensitivity to missing data assumptions: theory and an evaluation of the U.S. wage structure. [Patrick Kline; Andres Santos; National Bureau of Economic Research.] -- This paper develops practical methods for relaxing the missing at random assumption when estimating models of conditional quantiles with missing outcome data and discrete covariates.

Interval Estimation of Potentially Misspecified Quantile Models in the Presence of Missing Data of conditional quantiles with missing outcome data and discrete covariates.

of potentially. Estimation of quantiles in missing data models is a problem with applications to a variety of research areas. For example, policy makers may be interested in evaluating the effect of an educational program on the tails of the skill by: 7. Recently a regression model for quantile functions (not Koenker’s regression quantiles) was introduced for estimation of differences between two means from two sample data.

Estimating the ratio of two means from bivariate (normally distributed) data was perhaps first considered by Fieller [ 11 ], and bootstrap and bias corrected bootstrap. In this paper we consider (possibly misspecified) linear quantile regression models, and study a measure for the quality-of-fit of these models, (a version of which has been) previously proposed.

This paper considers several robust estimators for distribution functions and quantiles of a response variable when some responses may not be observed under the non-ignorable missing data mechanism.

As the level of confidence decreases, the size of the corresponding interval will decrease. Suppose the student was interested in a 90% confidence interval for the boiling temperature. In this case, ${\sigma = }$, and ${\frac{}{2} = }$.

The critical value for this level is equal toso the 90% confidence interval is. Downloadable. To date the literature on quantile regression and least absolute deviation regression has assumed either explicitly or implicitly that the conditional quantile regression model is correctly specified.

When the model is misspecified, confidence intervals and hypothesis tests based on the conventional covariance matrix are invalid. The missing at random (MAR) in sense of Rubin () is a common assumption for statistical analysis with missing data and is often reasonable in many practical situations.

A naive approach of handling missing data is a complete-case (C–C) analysis, which uses only data points with complete observation.

Marco Geraci, Estimation of regression quantiles in complex surveys with data missing at random: An application to birthweight determinants, Statistical Methods in Medical Research, /, 25, 4, (), (). Estimate in (20) is commonly reported (by, say, Stata). ∙ If the quantile function is misspecified, even the “robust” form of the variance matrix, based on the estimate in (20), is not valid.

In the generalized linear models literature, the distinction is sometimes made between a “fully robust” variance estimator and a “semi-robust.

6. Conclusion. In the present paper, we have derived two asymptotic approximations of the expected optimism, or the bias of the in-sample risk when used as an estimate of the predictive risk, and have proposed consistent estimates of the expected optimism and the predictive risk of potentially misspecified quantile regression models.

regression models with general quantile ranks and general set valued data. Some empirical pap ers (e.g., O’Garra and Mourato, ; Gamper-Rabindran and Timmins, ), however, use quan tiles.

Estimates of the fixed effects (standard errors) from two logistic quantile mixed-effects models with τ ∈ {0. 05, 0. 95} and from the normal nonlinear mixed-effects model (NLME) using the Soybean Data. Standard errors for quantile regression estimates are based on bootstrap replications.

Bold denotes statistically significant at the 5 %. Two-step combined nonparametric likelihood estimation of misspecified semiparametric models.

Bravo Published online: 28 Jul Article Testing for additivity in nonparametric heteroscedastic regression models. Zambom et al. Published A multiply robust Mann-Whitney test for non-randomised pretest-posttest studies with missing data.

Shixiao. Principled missing data methods for researchers. Missing data are a rule rather than an exception in quantitative research.

Enders () stated that a missing rate of 15% to 20% was common in educational and psychological et al. () surveyed quantitative studies published from to in 11 education and psychology journals. They found that 36% of. Manipulated Factors and Population Models.

We implemented a full factorial design with five between-subjects factors: uniformity of the mean and covariance structures, number of items per scale (8 or 16), sample size ( or ), item-level missing data rate (5%, 15%, 25%), and missing data mechanism (MCAR, MAR due to an variable external to the scales, and MAR due to complete items on the.

In this paper we consider the quantile regression based on the competing risks data with possibly missing failure R i be the complete-case indicator: R i = 1 either if δ i = 0 or if δ i = 1 and J i is observed; and R i = 0 otherwise. Auxiliary variables A i may be helpful for predicting the missing failure type.

Since the failure type is defined only for those who are observed to. Although values below or above a DL are “missing,” data are not missing at random in the usual sense, because their absence reflects levels of exposure. This type of missing data is called “nonignorable missing,” and the simple exclusion of such “interval-measured” data can bias results (Little and Rubin ; Schafer ).1.

Introduction. The estimators Q ̂ (u) of the population quantiles, 0 data.Distribution-free quantile estimators in parametric models; how much do we lose? 5. Optimal interval estimation 6.

Asymptotics References. 1 The Problem The problem of quantile estimation has a very long history and abundant literature: in out booklet we shall quote only .