This paper develops new econometric methods to infer hospital quality in a model with discrete de... more This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely used to infer hospital quality. However, hospital admission is not random and some hospitals may attract patients with greater unobserved severity of illness than others. In this situation the assumption of random
This paper develops new econometric methods to estimate hospital quality and other models with di... more This paper develops new econometric methods to estimate hospital quality and other models with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely used to infer hospital quality. However, hospital admission is not random and some hospitals may attract patients with greater unobserved severity of illness than others. In this situation the assumption of random
In this paper, a nonlinear and/or non-Gaussian smoother utilizing Markov chain Monte Carlo Method... more In this paper, a nonlinear and/or non-Gaussian smoother utilizing Markov chain Monte Carlo Methods is proposed, where the measurement and tran- sition equations are specified in any general formulation and the error terms in the state-space model are not necessarily normal. The random draws are directly generated from the smoothing densities. For random number genera- tion, the Metropolis-Hastings algorithm and
Statistical inference in multinomial multiperiod probit models has been hindered in the past by t... more Statistical inference in multinomial multiperiod probit models has been hindered in the past by the high dimensional numerical integrations necessary to form the likelihood functions, posterior distributions, or moment conditions in these models. We describe three alternative estimators, implemented using simulation-based approaches to inference, that circumvent the integration problem: posterior means computed using Gibbs sampling and data augmentation (GIBBS), simulated
The evaluation of a diagnostic test when the reference standard fails to establish a diagnosis in... more The evaluation of a diagnostic test when the reference standard fails to establish a diagnosis in some patients is a common and difficult analytical problem. Conventional operating characteristics, derived from a 2 x 2 matrix, require that tests have only positive or negative results, and that disease status be designated definitively as present or absent. Results can be displayed in a 2 x 3 matrix, with an additional column for undiagnosed patients, when it is not possible always to ascertain the disease status definitively. The authors approach this problem using a Bayesian method for evaluating the 2 x 3 matrix in which test operating characteristics are described by a joint probability density function. They show that one can derive this joint probability density function of sensitivity and specificity empirically by applying a sampling algorithm. The three-dimensional histogram resulting from this sampling procedure approximates the true joint probability density function for sensitivity and specificity. Using a clinical example, the authors illustrate the method and demonstrate that the joint probability density function for sensitivity and specificity can be influenced by assumptions used to interpret test results in undiagnosed patients. This Bayesian method represents a flexible and practical solution to the problem of evaluating test sensitivity and specificity when the study group includes patients whose disease could not be diagnosed by the reference standard.
This paper generalizes the normal probit model of dichotomous choice by introducing mixtures of n... more This paper generalizes the normal probit model of dichotomous choice by introducing mixtures of normals distributions for the disturbance term. By mixing on both the mean and variance parameters and by increasing the number of distributions in the mixture these models effectively remove the normality assumption and are much closer to semiparametric models. When a Bayesian approach is taken, there is an exact finite-sample distribution theory for the choice probability conditional on the covariates. The paper uses artificial data to show how posterior odds ratios can discriminate between normal and nonnormal distributions in probit models. The method is also applied to female labor force participation decisions in a sample with 1,555 observations from the PSID. In this application, Bayes factors strongly favor mixture of normals probit models over the conventional probit model, and the most favored models have mixtures of four normal distributions for the disturbance term.
Journal of the American Statistical Association, 1982
... the well-known chi square under the null hypothesis FY->x = 0, and may be approximated... more ... the well-known chi square under the null hypothesis FY->x = 0, and may be approximated under the alter-native. ... Fx y = Fy px + Fx y + FX y. The measure of linear dependence is the sum of the meas-ures of the three ... Journal of the American Statistical Association, June 1982 ...
This study uses data from the Panel Survey of Income Dynamics (PSID) to address a number of quest... more This study uses data from the Panel Survey of Income Dynamics (PSID) to address a number of questions about life cycle earnings mobility. It develops a dynamic reduced form model of earnings and marital status that is nonstationary over the life cycle. The study reaches ...
This paper discusses eight alternative tests of the absence of casual ordering, all of which are ... more This paper discusses eight alternative tests of the absence of casual ordering, all of which are asymptotically valid under the null hypothesis in the sense that their limiting size is known. Their behavior under alternatives is compared analytically using the concept of approximate ...
... Procedures for model selection are set forth which lead to the choice of the correct model wi... more ... Procedures for model selection are set forth which lead to the choice of the correct model with unit probability asymptotically. ... The most fre-quently studied case is that in which a distributed lag of unknown order is to be estimated. ...
This paper develops new econometric methods to infer hospital quality in a model with discrete de... more This paper develops new econometric methods to infer hospital quality in a model with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely used to infer hospital quality. However, hospital admission is not random and some hospitals may attract patients with greater unobserved severity of illness than others. In this situation the assumption of random
This paper develops new econometric methods to estimate hospital quality and other models with di... more This paper develops new econometric methods to estimate hospital quality and other models with discrete dependent variables and non-random selection. Mortality rates in patient discharge records are widely used to infer hospital quality. However, hospital admission is not random and some hospitals may attract patients with greater unobserved severity of illness than others. In this situation the assumption of random
In this paper, a nonlinear and/or non-Gaussian smoother utilizing Markov chain Monte Carlo Method... more In this paper, a nonlinear and/or non-Gaussian smoother utilizing Markov chain Monte Carlo Methods is proposed, where the measurement and tran- sition equations are specified in any general formulation and the error terms in the state-space model are not necessarily normal. The random draws are directly generated from the smoothing densities. For random number genera- tion, the Metropolis-Hastings algorithm and
Statistical inference in multinomial multiperiod probit models has been hindered in the past by t... more Statistical inference in multinomial multiperiod probit models has been hindered in the past by the high dimensional numerical integrations necessary to form the likelihood functions, posterior distributions, or moment conditions in these models. We describe three alternative estimators, implemented using simulation-based approaches to inference, that circumvent the integration problem: posterior means computed using Gibbs sampling and data augmentation (GIBBS), simulated
The evaluation of a diagnostic test when the reference standard fails to establish a diagnosis in... more The evaluation of a diagnostic test when the reference standard fails to establish a diagnosis in some patients is a common and difficult analytical problem. Conventional operating characteristics, derived from a 2 x 2 matrix, require that tests have only positive or negative results, and that disease status be designated definitively as present or absent. Results can be displayed in a 2 x 3 matrix, with an additional column for undiagnosed patients, when it is not possible always to ascertain the disease status definitively. The authors approach this problem using a Bayesian method for evaluating the 2 x 3 matrix in which test operating characteristics are described by a joint probability density function. They show that one can derive this joint probability density function of sensitivity and specificity empirically by applying a sampling algorithm. The three-dimensional histogram resulting from this sampling procedure approximates the true joint probability density function for sensitivity and specificity. Using a clinical example, the authors illustrate the method and demonstrate that the joint probability density function for sensitivity and specificity can be influenced by assumptions used to interpret test results in undiagnosed patients. This Bayesian method represents a flexible and practical solution to the problem of evaluating test sensitivity and specificity when the study group includes patients whose disease could not be diagnosed by the reference standard.
This paper generalizes the normal probit model of dichotomous choice by introducing mixtures of n... more This paper generalizes the normal probit model of dichotomous choice by introducing mixtures of normals distributions for the disturbance term. By mixing on both the mean and variance parameters and by increasing the number of distributions in the mixture these models effectively remove the normality assumption and are much closer to semiparametric models. When a Bayesian approach is taken, there is an exact finite-sample distribution theory for the choice probability conditional on the covariates. The paper uses artificial data to show how posterior odds ratios can discriminate between normal and nonnormal distributions in probit models. The method is also applied to female labor force participation decisions in a sample with 1,555 observations from the PSID. In this application, Bayes factors strongly favor mixture of normals probit models over the conventional probit model, and the most favored models have mixtures of four normal distributions for the disturbance term.
Journal of the American Statistical Association, 1982
... the well-known chi square under the null hypothesis FY->x = 0, and may be approximated... more ... the well-known chi square under the null hypothesis FY->x = 0, and may be approximated under the alter-native. ... Fx y = Fy px + Fx y + FX y. The measure of linear dependence is the sum of the meas-ures of the three ... Journal of the American Statistical Association, June 1982 ...
This study uses data from the Panel Survey of Income Dynamics (PSID) to address a number of quest... more This study uses data from the Panel Survey of Income Dynamics (PSID) to address a number of questions about life cycle earnings mobility. It develops a dynamic reduced form model of earnings and marital status that is nonstationary over the life cycle. The study reaches ...
This paper discusses eight alternative tests of the absence of casual ordering, all of which are ... more This paper discusses eight alternative tests of the absence of casual ordering, all of which are asymptotically valid under the null hypothesis in the sense that their limiting size is known. Their behavior under alternatives is compared analytically using the concept of approximate ...
... Procedures for model selection are set forth which lead to the choice of the correct model wi... more ... Procedures for model selection are set forth which lead to the choice of the correct model with unit probability asymptotically. ... The most fre-quently studied case is that in which a distributed lag of unknown order is to be estimated. ...
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Papers by John Geweke