Testing for seasonal unit roots by frequency domain regression

This paper develops univariate seasonal unit root tests based on spectral regression estimators. An advantage of the frequency domain approach is that it enables serial correlation to be treated non-parametrically. We demonstrate that our proposed statistics have pivotal limiting distributions under both the null and near seasonally integrated alternatives when we allow for weak dependence in the driving shocks. This is in contrast to the popular seasonal unit root tests of, among others, Hylleberg et al. (1990) which treat serial correlation parametrically via lag augmentation of the test regression. Moreover, our analysis allows for (possibly infinite order) moving average behaviour in the shocks, while extant large sample results pertaining to the Hylleberg et al. (1990) type tests are based on the assumption of a finite autoregression. The size and power properties of our proposed frequency domain regression-based tests are explored and compared for the case of quarterly data with those of the tests of Hylleberg et al. (1990) in simulation experiments.Seasonal unit root tests; moving average; frequency domain regression; spectral density estimator; Brownian motion

Noise thermometry applied to thermoelectric measurements in InAs nanowires

We apply noise thermometry to characterize charge and thermoelectric transport in single InAs nanowires (NWs) at a bath temperature of 4.2 K. Shot noise measurements identify elastic diffusive transport in our NWs with negligible electron-phonon interaction. This enables us to set up a measurement of the diffusion thermopower. Unlike in previous approaches, we make use of a primary electronic noise thermometry to calibrate a thermal bias across the NW. In particular, this enables us to apply a contact heating scheme, which is much more efficient in creating the thermal bias as compared to conventional substrate heating. The measured thermoelectric Seebeck coefficient exhibits strong mesoscopic fluctuations in dependence on the back-gate voltage that is used to tune the NW carrier density. We analyze the transport and thermoelectric data in terms of approximate Mott's thermopower relation and to evaluate a gate-voltage to Fermi energy conversion factor

Local noise in a diffusive conductor

The control and measurement of local non-equilibrium configurations is of utmost importance in applications on energy harvesting, thermoelectrics and heat management in nano-electronics. This challenging task can be achieved with the help of various local probes, prominent examples including superconducting or quantum dot based tunnel junctions, classical and quantum resistors, and Raman thermography. Beyond time-averaged properties, valuable information can also be gained from spontaneous fluctuations of current (noise). From these perspective, however, a fundamental constraint is set by current conservation, which makes noise a characteristic of the whole conductor, rather than some part of it. Here we demonstrate how to remove this obstacle and pick up a local noise temperature of a current biased diffusive conductor with the help of a miniature noise probe. This approach is virtually noninvasive and extends primary local measurements towards strongly non-equilibrium regimes.Comment: minor revision, accepted in Scientific Report

Watching the watchmen::a statistical analysis of mark consistency across taught modules

AbstractVerifying that taught modules are marked and taught to a common standard is important but doing so by comparing mean module marks is inadequate when students’ ability is not uniform across these modules. For example, a module taken by a group of students of above average ability may justifiably result in a high mean mark, without implying that inconsistent standards have been applied. We propose a modified version of the fixed effects regression that provides direct estimates of module mark biases while conditioning for student composition and requiring no additional, potentially confidential, information on students or staff. We describe how this modified fixed effects regression can be implemented on a set of student marks and how the results can be interpreted. Increases in student numbers and tuition fees have increased the preoccupation with, and monitoring of, marks. We show how one can generate statistics that are more informative of the biases in marking, while being explicit about their limitations

A systematic review of neuropsychiatric symptoms in mild cognitive impairment

Mild cognitive impairment (MCI) is a clinical concept proposed as an intermediate state between normal aging and dementia. This condition has multiple heterogeneous sources, including clinical presentation, etiology, and prognosis. Recently, the prevalence and associated features of neuropsychiatric symptoms (NPS) in MCI have been described. We systematically searched the PubMed database (last accessed on August 31, 2008) for articles on NPS in MCI. Included articles used strict selection criteria, and outcome variables were extracted in duplicate; of the 27 articles included, 14 (52%) used prospective cohorts. The global prevalence of NPS in MCI ranged from 35% to 85%. The most common behavioral symptoms were depression, anxiety, and irritability. Hospital-based samples reported a higher global prevalence of NPS than population-based studies; this discrepancy probably reflected differences in demographics, study setting, MCI diagnostic criteria, and behavioral instruments used. Prospective studies showed that NPS, particularly depression, may represent risk factors for MCI or predictors for the conversion of MCI to Alzheimer's disease (AD). NPS are very prevalent in subjects with MCI, displaying a similar pattern of symptoms compared to dementia and AD. Large cohort studies using standardized MCI criteria and behavioral instruments are required to evaluate the prognostic role of NPS in MCI.Mild cognitive impairment (MCI) is a clinical concept proposed as an intermediate state between normal aging and dementia. This condition has multiple heterogeneous sources, including clinical presentation, etiology, and prognosis. Recently, the prevalence and associated features of neuropsychiatric symptoms (NPS) in MCI have been described. We systematically searched the PubMed database (last accessed on August 31, 2008) for articles on NPS in MCI. Included articles used strict selection criteria, and outcome variables were extracted in duplicate; of the 27 articles included, 14 (52%) used prospective cohorts. The global prevalence of NPS in MCI ranged from 35% to 85%. The most common behavioral symptoms were depression, anxiety, and irritability. Hospital-based samples reported a higher global prevalence of NPS than population-based studies; this discrepancy probably reflected differences in demographics, study setting, MCI diagnostic criteria, and behavioral instruments used. Prospective studies showed that NPS, particularly depression, may represent risk factors for MCI or predictors for the conversion of MCI to Alzheimer’s disease (AD). NPS are very prevalent in subjects with MCI, displaying a similar pattern of symptoms compared to dementia and AD. Large cohort studies using standardized MCI criteria and behavioral instruments are required to evaluate the prognostic role of NPS in MC