The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.

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Econometric Analysis of Cross Section and Panel Data

With explanatory covariates, the standard analysis for competing risks data involves modeling the cause-specific hazard functions via a proportional hazards assumption Unfortunately, the cause-specific hazard function does not have a direct interpretation in terms of survival probabilities for the particular failure type In recent years many clinicians have begun using the cumulative incidence function, the marginal failure probabilities for a particular cause, which is intuitively appealing and more easily explained to the nonstatistician The cumulative incidence is especially relevant in cost-effectiveness analyses in which the survival probabilities are needed to determine treatment utility Previously, authors have considered methods for combining estimates of the cause-specific hazard functions under the proportional hazards formulation However, these methods do not allow the analyst to directly assess the effect of a covariate on the marginal probability function In this article we pro

http://www.clinicalpredictionmodels.org/lib/exe/fetch.php?id=additional%3Achapter04&cache=cache&media=additional:fine_and_gray_competing_risk_1999.pdf

A Proportional Hazards Model for the Subdistribution of a Competing Risk

Background Traditional approaches to mechanical ventilation use tidal volumes of 10 to 15 ml per kilogram of body weight and may cause stretch-induced lung injury in patients with acute lung injury and the acute respiratory distress syndrome. We therefore conducted a trial to determine whether ventilation with lower tidal volumes would improve the clinical outcomes in these patients. Methods Patients with acute lung injury and the acute respiratory distress syndrome were enrolled in a multicenter, randomized trial. The trial compared traditional ventilation treatment, which involved an initial tidal volume of 12 ml per kilogram of predicted body weight and an airway pressure measured after a 0.5-second pause at the end of inspiration (plateau pressure) of 50 cm of water or less, with ventilation with a lower tidal volume, which involved an initial tidal volume of 6 ml per kilogram of predicted body weight and a plateau pressure of 30 cm of water or less. The primary outcomes were death before a patient was discharged home and was breathing without assistance and the number of days without ventilator use from day 1 to day 28. Results The trial was stopped after the enrollment of 861 patients because mortality was lower in the group treated with lower tidal volumes than in the group treated with traditional tidal volumes (31.0 percent vs. 39.8 percent, P=0.007), and the number of days without ventilator use during the first 28 days after randomization was greater in this group (mean [+/-SD], 12+/-11 vs. 10+/-11; P=0.007). The mean tidal volumes on days 1 to 3 were 6.2+/-0.8 and 11.8+/-0.8 ml per kilogram of predicted body weight (P Conclusions In patients with acute lung injury and the acute respiratory distress syndrome, mechanical ventilation with a lower tidal volume than is traditionally used results in decreased mortality and increases the number of days without ventilator use.

Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome.

This book describes the new generation of discrete choice methods, focusing on the many advances that are made possible by simulation. Researchers use these statistical methods to examine the choices that consumers, households, firms, and other agents make. Each of the major models is covered: logit, generalized extreme value, or GEV (including nested and cross-nested logits), probit, and mixed logit, plus a variety of specifications that build on these basics. Simulation-assisted estimation procedures are investigated and compared, including maximum simulated likelihood, method of simulated moments, and method of simulated scores. Procedures for drawing from densities are described, including variance reduction techniques such as anithetics and Halton draws. Recent advances in Bayesian procedures are explored, including the use of the Metropolis-Hastings algorithm and its variant Gibbs sampling. No other book incorporates all these fields, which have arisen in the past 20 years. The procedures are applicable in many fields, including energy, transportation, environmental studies, health, labor, and marketing.

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Discrete Choice Methods with Simulation

Background In patients with high cholesterol levels, lowering the cholesterol level reduces the risk of coronary events, but the effect of lowering cholesterol levels in the majority of patients with coronary disease, who have average levels, is less clear. Methods In a double-blind trial lasting five years, we administered either 40 mg of pravastatin per day or placebo to 4159 patients (3583 men and 576 women) with myocardial infarction who had plasma total cholesterol levels below 240 mg per deciliter (mean, 209) and low-density lipoprotein (LDL) cholesterol levels of 115 to 174 mg per deciliter (mean, 139). The primary end point was a fatal coronary event or a nonfatal myocardial infarction. Results The frequency of the primary end point was 10.2 percent in the pravastatin group and 13.2 percent in the placebo group, an absolute difference of 3 percentage points and a 24 percent reduction in risk (95 percent confidence interval, 9 to 36 percent; P = 0.003). Coronary bypass surgery was needed in 7.5 per...

The Effect of Pravastatin on Coronary Events after Myocardial Infarction in Patients with Average Cholesterol Levels

The Statistical Analysis of Failure Time Data.

Maximum likelihood estimation of item parameters in the marginal distribution, integrating over the distribution of ability, becomes practical when computing procedures based on an EM algorithm are used By characterizing the ability distribution empirically, arbitrary assumptions about its form are avoided The Em procedure is shown to apply to general item-response models lacking simple sufficient statistics for ability This includes models with more than one latent dimension

Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm.

Part 1 Introducing GLIM 3: getting started in GLIM 3. Part 2 Statistical modelling and statistical inference: the Bernoulli distribution for binary data types of variables definition of a statistical model model criticism likelihood-based confidence intervals. Part 3 Normal regression and analysis of variance: the normal distribution and the Box-Cox transformation family link functions and transformations regression models for prediction the use of regression models for calibration fatorial designs midding data. Part 4 Binomial response data: binary responses transformations and link functions contingency table construction from binary data multidimensional contingency tables with a binary response. Part 5: multinomial and Poisson response data. Part 6 Survival data: probability plotting with censored data - the Kaplan-Meier estimator the Weibull distribution the Cox proportional hazards model and the piecewise exponential distribution the logistic and log logistic distributions time-dependent explanatory variables. Appendices: discussion GLIM directives system defined structures in GLIM datasets and macros.

Statistical Modelling in GLIM

SUMMARY The assessment of school effectiveness in educational research studies is considered from the viewpoint of statistical modelling. A variety of models are applied to a set of data on 907 pupils in 18 schools from one Local Education Authority. We argue for the general use of variance component or "random parameter" models for the analysis of such studies involving clustered observations. For the data examined, the model which regresses school mean outcome on school mean intake (i.e. the "means on means" model) is shown to give estimated school effects considerably different from those produced by other models. In the light of our results, we comment on several recent large-scale British studies of school effectiveness.

https://rss.onlinelibrary.wiley.com/doi/pdf/10.2307/2981882

Statistical Modelling Issues in School Effectiveness Studies

/pdf/marginal-maximum-likelihood-estimation-of-item-parameters-4lxwdtwu75.pdf

Murray Aitkin

Papers

The Statistical Analysis of Failure Time Data.

Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm.

Statistical Modelling in GLIM

Statistical Modelling Issues in School Effectiveness Studies

Marginal maximum likelihood estimation of item parameters