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We propose a new integrated likelihood based approach for estimating panel data models when the unobserved individual effects enter the model nonlinearly. Unlike existing integrated likelihoods in the literature, the one we propose is closer to a genuine likelihood. Although the statistical theory for the proposed estimator is developed in an asymptotic setting where the number of individuals and the number of time periods both approach infinity, results from a simulation study suggest that our methodology can work very well even in moderately sized panels of short duration in both static and dynamic models.

Consider a two-dimensional random vector (X, Y )T. Let Q0, Q1,… denote orthogonal polynomials with respect to the marginal distribution of X and let P0, P1,… denote orthogonal polynomials with respect to the marginal distribution of Y. In this paper, identities of the form E[Pn(Y )|X] = anQn(X), for constants a0, a1,… are considered and necessary and sufficient conditions for this type of identity to hold are given,. The application of the identity to the maximal correlation of two random variables and to the L2 completeness of a bivariate distribution are discussed.

Laboratory studies have demonstrated that circadian clocks align physiology and behavior to 24-h environmental cycles. Examination of athletic performance has been used to discern the functions of these clocks in humans outside of controlled settings. Here, we examined the effects of jet lag, that is, travel that shifts the alignment of 24-h environmental cycles relative to the endogenous circadian clock, on specific performance metrics in Major League Baseball. Accounting for potential differences in home and away performance, travel direction, and team confounding variables, we observed that jet-lag effects were largely evident after eastward travel with very limited effects after westward travel, consistent with the >24-h period length of the human circadian clock. Surprisingly, we found that jet lag impaired major parameters of home-team offensive performance, for example, slugging percentage, but did not similarly affect away-team offensive performance. On the other hand, jet lag impacted both home and away defensive performance. Remarkably, the vast majority of these effects for both home and away teams could be explained by a single measure, home runs allowed. Rather than uniform effects, these results reveal surprisingly specific effects of circadian misalignment on athletic performance under natural conditions.

In the market model the return on an asset is modeled as a linear function of the return on a market index with slope parameter beta. The coefficient beta is often used as a measure of the sensitivity of the asset’s return to the market and to measure the component of the variance of the return that is explained by the market. However, both of these interpretations require the additional assumption that the error term in the market model has mean 0 conditional on the return on the market index, an assumption that is often difficult to verify in practice. In this paper, a nonparametric version of the market model is proposed that does not require such an assumption. This nonparametric model replaces the beta coefficient of the market model with a “beta curve” describing the relationship between the asset’s return and that of the market locally near a given value of the market return. The proposed model is applied to stock returns, as well as to returns on mutual funds. Corresponding tests of the market model are given and it is shown that the nonparametric model often provides an improvement over the standard parametric market model.

We propose a flexible model for correlated medical cost data with several appealing features. First, the mean function is partially linear. Second, the distributional form for the response is not specified. Third, the covariance structure of correlated medical costs has a semiparametric form. We use extended generalized estimating equations to simultaneously estimate all parameters of interest. B-splines are used to estimate unknown functions, and a modification to Akaike information criterion is proposed for selecting knots in spline bases. We apply the model to correlated medical costs in the Medical Expenditure Panel Survey dataset. Simulation studies are conducted to assess the performance of our method.

In our paper, “How jet lag impairs Major League Baseball performance,” we describe performance reductions in teams as a function of travel across time zones and attribute these effects to jet lag (1). The letter by Joly (2) raises the issue of correlation versus causation, implying that we cannot conclude that jet lag causes these effects. In cases where laboratory studies or prospective controlled intervention trials are not possible or are difficult, retrospective studies in natural settings can be valuable. Nonetheless, … [↵][1]1To whom correspondence should be addressed. Email: r-allada{at}northwestern.edu. [1]: #xref-corresp-1-1

Consider a linear semiparametric regression model with normal errors in which the mean function depends on two parameters, a p-dimensional regression parameter, which is the parameter of interest, and an unknown function, which is a nuisance parameter. We consider estimation of the parameter of interest using an integrated likelihood function, in which the nuisance parameter is eliminated from the likelihood function by averaging with respect to some distribution. Here we take this distribution to be a Gaussian process with a given covariance function, which may depend on additional parameters. Likelihood inference based on the resulting integrated likelihood is considered and the properties of the score statistic based on the integrated likelihood, the maximum integrated likelihood estimator, and the integrated likelihood ratio statistic are presented. The methodology is illustrated on two examples.

An integrated likelihood depends only on the parameter of interest and the data, so it can be used as a standard likelihood function for likelihood-based inference. In this paper, the higher-order asymptotic properties of the signed integrated likelihood ratio statistic for a scalar parameter of interest are considered. These results are used to construct a modified integrated likelihood ratio statistic and to suggest a class of prior densities to use in forming the integrated likelihood. The properties of the integrated likelihood ratio statistic are compared to those of the standard likelihood ratio statistic. Several examples show that the integrated likelihood ratio statistic can be a useful alternative to the standard likelihood ratio statistic. Copyright 2010, Oxford University Press.

Consider a model with parameter θ = (ψ, λ), where ψ is the parameter of interest, and let L(ψ, λ) denote the likelihood function. One approach to likelihood inference for ψ is to use an integrated likelihood function, in which λ is eliminated from L(ψ, λ) by integrating with respect to a density function π(λ|ψ). The goal of this paper is to consider the problem of selecting π(λ|ψ) so that the resulting integrated likelihood function is useful for non-Bayesian likelihood inference. The desirable properties of an integrated likelihood function are analyzed and these suggest that π(λ|ψ) should be chosen by finding a nuisance parameter ϕ that is unrelated to ψ and then taking the prior density for ϕ to be independent of ψ. Such an unrelated parameter is constructed and the resulting integrated likelihood is shown to be closely related to the modified profile likelihood. Copyright 2007, Oxford University Press.

Higher-order approximations to the distribution of the likelihood ratio statistic are considered for a class of nonregular models in which the maximum likelihood estimator of the parameter of interest is asymptotically distributed according to an exponential, rather than a normal, distribution. Asymptotic behaviour of this type often arises when the boundary of the support of the distributions under consideration depends on 0. A modified likelihood ratio statistic is proposed that follows its asymptotic distribution to a high degree of approximation, and this statistic is illustrated on several examples.

Summary The TDT provides a hypothesis test for the presence of linkage or association (linkage disequilibrium). However, since the TDT is a single test statistic, it cannot be used to separate association and linkage. The importance of this difficulty, following a significant TDT result, has been recently emphasized by Whittaker, Denham and Morris (2000), who alert the community to the possibility that a significant TDT may result from loose linkage and strong association, or from tight linkage and weak association. To attack this problem we start with the parametric model for family-based allele transmission data of Sham and Curtis (1995) (or Sham (1998)) and find that the parameters in the model are not always identifiable. So we introduce a reparameterization that resolves the identifiability issues and leads to a valid likelihood ratio (LR) test for linkage. Since the linkage and association parameters are both of interest, we next introduce and apply an integrated likelihood (IL) approach to provide separate point estimates and confidence intervals for these parameters. The estimates are shown to have generally small bias and mean square error, while the confidence intervals have good average length and coverage probabilities. We compare the power of the IL approach for testing linkage and, separately association, with the TDT and LR.

Frequentist inference about a parameter of interest in presence of a nuisance parameter can be based on an integrated likelihood function. We analyze the behaviour of inferential quantities based on such a pseudo-likelihood in a two-index-asymptotics framework, in which both sample size and dimension of the nuisance parameter may diverge to infinity. We show that a properly chosen integrated likelihood largely outperforms standard likelihood methods, such as those based on the profile likelihood. These results are confirmed by simulation studies, in which comparisons with modified profile likelihood are also considered.

SUMMARY The paper considers priors which are right invariant with respect to the Haar measure. It is shown that the posterior coverage probabilities of certain invariant Bayesian predictive regions exactly match the corresponding frequentist probabilities. Several examples are given to illustrate the main result.

In a parametric model the maximum likelihood estimator of a parameter of interest ψ may be viewed as the solution to the equation l' p (ψ)=0, where l P denotes the profile loglikelihood function. It is well known that the estimating function l' p (ψ) is not unbiased and that this bias can, in some cases, lead to poor estimates of ψ. An alternative approach is to use the modified profile likelihood function, or an approximation to the modified profile likelihood function, which yields an estimating function that is approximately unbiased. In many cases, the maximum likelihood estimating functions are unbiased under more general assumptions than those used to construct the likelihood function, for example under first- or second-moment conditions. Although the likelihood function itself may provide valid estimates under moment conditions alone, the modified profile likelihood requires a full parametric model. In this paper, modifications to l' p (ψ ) are presented that yield an approximately unbiased estimating function under more general conditions.

A goodness-of-fit technique for random samples from the exponential distribution based on the sample Lorenz curve is adapted for use in the exponential order statistic (EOS) model. In the EOS model, only those observations in a random sample from the exponential distribution of unknown size N that are less than some known stopping time T are observable. The model is known as the Jelinski-Moranda model in software reliability, where it is used to estimate the number of bugs in software during development. Distributional results are derived for the distance between the sample Lorenz curve and the population Lorenz curve so that it can be used as a goodness-of-fit test statistic. Simulations show that the test has good power against several alternative distributions. Simulations also indicate that in some cases, model misspecification leads to poor parameter estimation. A plotting procedure provides a means of graphical assessment of fit.