Exponential Conditional Volatility Models

The asymptotic distribution of maximum likelihood estimators is derived for a class of exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models. The result carries over to models for duration and realised volatility that use an exponential link function. A key feature of the model formulation is that the dynamics are driven by the score

Tests of time-invariance

Quantiles provide a comprehensive description of the properties of a variable and tracking changes in quantiles over time using signal extraction methods can be informative. It is shown here how stationarity tests can be generalized to test the null hypothesis that a particular quantile is constant over time by using weighted indicators. Corresponding tests based on expectiles are also proposed; these might be expected to be more powerful for distributions that are not heavy-tailed. Tests for changing dispersion and asymmetry may be based on contrasts between particular quantiles or expectiles. We report Monte Carlo experiments investigating the effectiveness of the proposed tests and then move on to consider how to test for relative time invariance, based on residuals from fitting a time-varying level or trend. Empirical examples, using stock returns and U.S. inflation, provide an indication of the practical importance of the tests

“…the point is to change it”: Critical realism and human geography

This paper picks up themes discussed in Cox’s (2013) ‘Notes on a brief encounter: critical realism, historical materialism and human geography’. I argue that perhaps the encounter was more complex than Cox allows. At core Cox underplays, or marginalises, the discussion of causality and dismisses the significance of ontology stressing instead epistemology. The paper makes a case for another reading of the debate, one that has continuing significance

Weight Trajectories from Birth and Bone Mineralization at 7 Years of Age

Objective: To assess whether different trajectories of weight gain since birth influence bone mineral content (BMC) and areal bone mineral density (aBMD) at 7 years of age. Study design: We studied a subsample of 1889 children from the Generation XXI birth cohort who underwent whole-body dual-energy radiograph absorptiometry. Weight trajectories identified through normal mixture modeling for model-based clustering and labeled “normal weight gain,” “weight gain during infancy,” “weight gain during childhood,” and “persistent weight gain” were used. Differences in subtotal BMC, aBMD, and size-corrected BMC (scBMC) at age 7 years according to weight trajectories were estimated through analysis of covariance. Results: Compared with the “normal weight gain” trajectory, children in the remaining trajectories had significantly greater BMC, aBMD, and scBMC at age 7 years, with the strongest associations for “persistent weight gain” (girls [BMC: 674.0 vs 559.8 g, aBMD: 0.677 vs 0.588 g/cm2, scBMC: 640.7 vs 577.4 g], boys [BMC: 689.4 vs 580.8 g, aBMD: 0.682 vs 0.611 g/cm2, scBMC: 633.0 vs 595.6 g]). After adjustment for current weight, and alternatively for fat and lean mass, children with a “weight gain during childhood” trajectory had greater BMC and aBMD than those with a “normal weight gain” trajectory, although significant differences were restricted to girls (BMC: 601.4 vs 589.2 g, aBMD: 0.618 vs 0.609 g/cm2). Conclusion: Overall, children following a trajectory of persistent weight gain since birth had clearly increased bone mass at 7 years, but weight gain seemed slightly more beneficial when it occurred later rather than on a normal trajectory during the first 7 years of life

Metal-based imaging agents: progress towards interrogating neurodegenerative disease.

Central nervous system (CNS) neurodegeneration is defined by a complex series of pathological processes that ultimately lead to death. The precise etiology of these disorders remains unknown. Recent efforts show that a mechanistic understanding of the malfunctions underpinning disease progression will prove requisite in developing new treatments and cures. Transition metals and lanthanide ions display unique characteristics (i.e., magnetism, radioactivity, and luminescence), often with biological relevance, allowing for direct application in CNS focused imaging modalities. These techniques include positron emission tomography (PET), single-photon emission computed tomography (SPECT), magnetic resonance imaging (MRI), and luminescent-based imaging (LumI). In this Tutorial Review, we have aimed to highlight the various metal-based imaging techniques developed in the effort to understand the pathophysiological processes associated with neurodegeneration. Each section has been divided so as to include an introduction to the particular imaging technique in question. This is then followed by a summary of key demonstrations that have enabled visualization of a specific neuropathological biomarker. These strategies have either exploited the high binding affinity of a receptor for its corresponding biomarker or a specific molecular transformation caused by a target species, all of which produce a concomitant change in diagnostic signal. Advantages and disadvantages of each method with perspectives on the utility of molecular imaging agents for understanding the complexities of neurodegenerative disease are discussed

Adaptive Evolutionary Clustering

In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naive estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios.Comment: To appear in Data Mining and Knowledge Discovery, MATLAB toolbox available at http://tbayes.eecs.umich.edu/xukevin/affec