We describe a novel spatially and temporally detailed approach for determining the cause or causes of a population decline, using the western Alaskan population of Steller sea lions (Eumetopias jubatus) as an example. Existing methods are mostly based on regression, which limits their utility when there are multiple hypotheses to consider and the data are sparse and noisy. Our likelihood-based ...

The behavior of maximum likelihood estimates (MLEs) and the likelihood ratio statistic in a family of problems involving pointwise nonparametric estimation of a monotone function is studied. This class of problems differs radically from the usual parametric or semiparametric situations in that the MLE of the monotone function at a point converges to the truth at rate n (slower than the usual √ ...

Mixed Poisson regression models, a class of generalized linear mixed models, are commonly used to analyze count data that exhibit overdispersion. Because inference for these models can be computationally difficult, simplifying distributional assumptions are often made. We consider an influence function representing effects of infinitesimal perturbations of the mixing distribution. This function...

This study investigates the missing data problem in the Japan Meteorological Agency catalog of the Kumamoto aftershock sequence, which occurred since April 15, 2016, in Japan. Based on the assumption that earthquake magnitudes are independent of their occurrence times, we replenish the short-term missing data of small earthquakes by using a bi-scale transformation and study their influence on t...

The issue of normalization arises whenever two different values for a vector of unknown parameters imply the identical economic model. A normalization implies not just a rule for selecting which among equivalent points to call the maximum likelihood estimate (MLE), but also governs the topography of the set of points that go into a small-sample confidence interval associated with that MLE. A po...

A model-based multiple imputation approach for analyzing sample data with non-detects is proposed. The imputation approach involves randomly generating observations below the detection limit using the detected sample values and then analyzing the data using complete sample techniques, along with suitable adjustments to account for the imputation. The method is described for the normal case and ...

We consider a distributed system where sensors make location parameter estimates using their observations. A central processor collects the local estimates and declares a nal estimate based on them. We present a simple study of the convergence properties of three structures where empirical mean and M-estimates are used in various combinations. It is shown that when occasional outliers exist, de...

In this paper we investigate the large-sample behavior of the maximum likelihood estimate (MLE) of the unknown parameter θ for processes following the model dξt = θf(t)ξt dt+ dBt where f : R → R is a continuous function with period, say P > 0, and which is observed through continuous time interval [0, T ] as T → ∞. Here the periodic function f(·) is assumed known. We establish the consistency o...

Moody’s KMV method is a popular commercial implementation of the structural credit risk model pioneered by Merton (1974). It is an algorithm for estimating the unobserved asset value and the unknown parameters required for implementing such a model. This estimation method has found its way to the recent academic literature, but it has not yet been formally analyzed to assess its statistical pro...

This paper introduces a new class of robust estimates for ARMA models. They are M-estimates, but the residuals are computed so the effect of one outlier is limited to the period where it occurs. These estimates are closely related to those based on a robust filter, but they have two important advantages: they are consistent and the asymptotic theory is tractable. We perform a Monte Carlo where ...