The generalized extreme value (GEV) distribution is often fitted to environmental time series of extreme values such as annual maxima of daily precipitation. We study two methodological issues here. First, we compare criteria for selecting the best model among 16 GEV models that allow nonstationary scale and location parameters. Simulation results showed that both the corrected Akaike information criterion and Bayesian information criterion (BIC) always detected nonstationarity, but the BIC selected the correct model more often except in very small samples. Second, we examined confidence intervals (CIs) for model parameters and other quantities such as the return levels that are usually required for hydrological and climatological time series. Four bootstrap CIs-normal, percentile, basic and bias-corrected and accelerated-constructed by random-t resampling, fixed-t resampling and the parametric bootstrap methods were compared. CIs for parameters of the stationary model do not present major differences. CIs for the more extreme quantiles tend to become very wide for all bootstrap methods. For nonstationary GEV models with linear time dependence of location or log-linear time dependence of scale, CI coverage probabilities are reasonably accurate for the parameters. For the extreme percentiles, the bias-corrected and accelerated method is best overall, and the fixed-t method also has good average coverage probabilities. A case study is presented of annual maximum daily precipitation over the mountainous Mesochora catchment in Greece. Analysis of historical data and data generated under two climate scenarios (control run and climate change) supported a stationary GEV model reducing to the Gumbel distribution.

Background: The outbreak of the COVID-19 pandemic seems to have mental health implications for both people with neurocognitive disorder and their caregivers. Objective: The study aimed to shed light on relations between caregiver mental reaction to the pandemic and caregiver distress related to neuropsychiatric symptoms, memory impairment progression, and functional impairment of people with neurocognitive disorder during the period of confinement in Greece. Methods: The study included caregivers of patients with mild (N = 13) and major (N = 54) neurocognitive disorder. The caregiver-based telephone interview was based on items of the neuropsychiatric inventory questionnaire, the AD8 Dementia Screening Instrument, and the Bristol Activities of Daily Living Scale. Regarding the mental impact of the COVID-19 crisis on caregivers, four single questions referring to their worries in the last seven days were posed, in addition to the scales Generalized Anxiety Disorder 7-Item (GAD-7) and the 22-item Impact of Event Scale-revised (IES-R). A stepwise linear regression model was employed for studying the relationship between caregiver distress and demographic and clinical data and caregiver mental reaction to COVID-19 pandemic outbreak. Results: Caregiver distress severity during the confinement period was influenced not only by memory deficits (p = 0.009) and neuropsychiatric symptoms (p <  0.001) of patients, but also by caregiver hyperarousal (p = 0.003) and avoidance symptoms (p = 0.033) and worries directly linked to the COVID-19 crisis (p = 0.022). Conclusion: These observations provide further evidence for the urgent need for support of caregivers of patients with neurocognitive disorder during the COVID-19 pandemic.

Background/Aims: The utility of β-site amyloid-β precursor protein (AβPP) cleaving enzyme 1 (BACE1) activity and soluble AβPP β (sAβPPβ) levels in cerebrospinal fluid (CSF) in detecting Alzheimer’s disease (AD) is still elusive. Methods: BACE1 activity and sAβPPβ concentration were measured in patients with AD dementia (n = 56) and mild cognitive impairment (MCI) due to AD (n = 76) with abnormal routine AD CSF markers, in patients with MCI with normal CSF markers (n = 39), and in controls without preclinical AD (n = 48). In a subsample with available ¹⁸F-fluorodeoxyglucose positron emission tomography (FDG PET) data, ordinal regression models were employed to compare the contribution of BACE1 and sAβPPβ to correct diagnostic classification to that of FDG PET. Results: BACE1 activity was significantly higher in patients with MCI due to AD compared to both controls and patients with MCI with normal CSF markers. sAβPPβ did not differ between any of the studied groups. Interestingly, BACE1 activity was not found to be inferior to FDG PET as predictive covariate in differentiating between the diagnostic groups. Conclusions: Further studies using biomarker-underpinned diagnoses are warranted to shed more light on the potential diagnostic utility of BACE1 activity as AD biomarker candidate in MCI.

The power function distribution is often used to study the electrical component reliability. In this paper, we model a heterogeneous population using the two-component mixture of the power function distribution. A comprehensive simulation scheme including a large number of parameter points is followed to highlight the properties and behavior of the estimates in terms of sample size, censoring rate, parameters size and the proportion of the components of the mixture. The parameters of the power function mixture are estimated and compared using the Bayes estimates. A simulated mixture data with censored observations is generated by probabilistic mixing for the computational purposes. Elegant closed form expressions for the Bayes estimators and their variances are derived for the censored sample as well as for the complete sample. Some interesting comparison and properties of the estimates are observed and presented. The system of three non-linear equations, required to be solved iteratively for the computations of maximum likelihood (ML) estimates, is derived. The complete sample expressions for the ML estimates and for their variances are also given. The components of the information matrix are constructed as well. Uninformative as well as informative priors are assumed for the derivation of the Bayes estimators. A real-life mixture data example has also been discussed. The posterior predictive distribution with the informative Gamma prior is derived, and the equations required to find the lower and upper limits of the predictive intervals are constructed. The Bayes estimates are evaluated under the squared error loss function.information matrix, censored sampling, inverse transform method, squared error loss function, predictive distribution,

Background: Telephone-based neurocognitive instruments embody valuable tools in identifying cognitive impairment in research settings and lately also in clinical contexts due to the pandemic crisis. The accuracy of the Cognitive Telephone Screening Instrument (COGTEL) in detecting mild- (MiND) and major (MaND) neurocognitive disorder has not been studied yet. Objective: Comparison of the utility of COGTEL and COGTEL+, which is enriched with orientation items, with the modified Mini-Mental State Examination (3MS) in detecting MiND and MaND due to Alzheimer’s disease (AD) and assessment of the impact of COGTEL face-to-face-versus telephone administration on individual performance. Methods: The study included 197 cognitively intact individuals (CI), being at least 45 years old, 95 and 65 patients with MiND and MaND due to AD, respectively. In 20 individuals COGTEL was administered both in face-to-face and telephone sessions. Statistical analyses included proportional odds logistic regression models, stratified repeated random subsampling used to recursive partitioning to training and validation set (70/30 ratio), and an appropriate F-test. Results: All studied instruments were significant predictors of diagnostic outcome, but COGTEL+ and 3MS explained more variance relative to the original COGTEL. Except for the validation regression models including COGTEL in which the average misclassification error slightly exceeded 15%, in all other cases the average misclassification errors (%) were lower than 15%. COGTEL administration modality was not related to systematic over- or underestimation of performance on COGTEL. Conclusion: COGTEL+ is a valuable instrument in detecting MiND and MaND and can be administered in face-to-face or telephone sessions.