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Sea level rise can bring disastrous outcomes to people living in coastal regions by increasing flood risk or inducing stronger storm surges. We study long-term linear trends in monthly maximum sea levels by applying extreme value methods. The monthly maximum sea levels are extracted from multiple tide gauges around the coastal regions of the world over a period of as long as 169 years. Due to instrument changes, location changes, earthquakes, land reclamation, dredging, etc., the sea level data could contain inhomogeneous shifts in their means, which can substantially impact trend estimates if ignored. To rigorously quantify the long-term linear trends and return levels for the monthly maximum sea level data, we use a genetic algorithm to estimate the number and times of changepoints in the data. As strong periodicity and temporal correlation are pertinent to the data, bootstrap techniques are used to obtain more realistic confidence intervals to the estimated trends and return levels. We find that the consideration of changepoints changed the estimated linear trends of 89 tide gauges (approximately 30% of tide gauges considered) by more than 20cmcentury-1. Our results are summarized in maps with estimated extreme sea level trends and 50-year return levels.

A major winter storm brought up to 42 inches of snow in parts of the Mid-Atlantic and Northeast states for January 22–24, 2016. The blizzard of January 2016 impacted about 102.8 million people, cla...

Prescribed fire has long been used worldwide for livestock and wildlife management. Prescribed fire effects on activity budgets and movement path characteristics of grazing animals, however, remain largely unknown. We evaluated whether prescribed-fire treatments in mesic sagebrush steppe affect cattle behaviors, which could influence foraging efficiency and, ultimately, impact animal productivity.Mature, lactating beef cows grazing within two study areas located in the Owyhee Mountains of southwestern Idaho, United States were tracked with Global Positioning System collars for 2 yr before and 5 yr after fall prescribed fire. Tracking data were then classified into stationary, foraging, and traveling activity types on the basis of movement distance and velocity thresholds. After fire, cattle in burned sites tended to increase their foraging activity budgets, bout durations, bout counts, and path lengths relative to prefire and cattle in unburned sites. Fire did not affect steepness of cattle foraging paths. Prescribed fire in mesic sagebrush steppe can be used to create opportunities for cattle to improve foraging efficiency by altering their activity budgets and movement path characteristics. Any consequent improvements in foraging efficiency could, in turn, promote increased rates of weight gain, better body condition, enhanced reproductive success, and ultimately, more pounds of beef for market.

This paper develops methods to estimate a long‐term trend in a daily snow depth record. The methods use a storage equation model for the daily snow depths that allows for seasonality, support set features (snow depths cannot be negative), correlation, and mean level shift changepoint features. Changepoints can occur in snow processes whenever observing stations move or station instrumentation is changed; they are critical features to consider when estimating a long‐term trend. A likelihood objective function is developed for the storage model and is used to estimate model parameters. Genetic algorithms are used to optimize a minimum descriptive length model selection criterion that estimates the changepoint numbers and locations. The methods are applied in the analysis of a daily series recorded near Warm Lake, Idaho, from 1948 to 2009.

This study evaluates the performance of NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) precipitation product in reproducing the trend and distribution of extreme precipitation events. Utilizing the extreme value theory, time-invariant and time-variant extreme value distributions are developed to model the trends and changes in the patterns of extreme precipitation events over the contiguous United States during 1979–2010. The Climate Prediction Center (CPC) U.S. Unified gridded observation data are used as the observational dataset. The CPC analysis shows that the eastern and western parts of the United States are experiencing positive and negative trends in annual maxima, respectively. The continental-scale patterns of change found in MERRA seem to reasonably mirror the observed patterns of change found in CPC. This is not previously expected, given the difficulty in constraining precipitation in reanalysis products. MERRA tends to overestimate the frequency at which the 99th percentile of precipitation is exceeded because this threshold tends to be lower in MERRA, making it easier to be exceeded. This feature is dominant during the summer months. MERRA tends to reproduce spatial patterns of the scale and location parameters of the generalized extreme value and generalized Pareto distributions. However, MERRA underestimates these parameters, particularly over the Gulf Coast states, leading to lower magnitudes in extreme precipitation events. Two issues in MERRA are identified: 1) MERRA shows a spurious negative trend in Nebraska and Kansas, which is most likely related to the changes in the satellite observing system over time that has apparently affected the water cycle in the central United States, and 2) the patterns of positive trend over the Gulf Coast states and along the East Coast seem to be correlated with the tropical cyclones in these regions. The analysis of the trends in the seasonal precipitation extremes indicates that the hurricane and winter seasons are contributing the most to these trend patterns in the southeastern United States. In addition, the increasing annual trend simulated by MERRA in the Gulf Coast region is due to an incorrect trend in winter precipitation extremes.

This article considers short memory characteristics in a long memory process. We derive new asymptotic results for the sample autocorrelation difference ratios. We used these results to develop a new portmanteau test that determines if short memory parameters are statistically significant. In simulations, the new test can detect short memory components more often than the Ljung-Box test when these short memory components are in fact within a long memory process. Interestingly, our test finds short memory autocorrelations in U.S. inflation rate data, whereas the Ljung-Box test fails to find these autocorrelations. Modeling these short memory autocorrelations of the inflation rate data leads to improved model accuracy and more precise prediction.

Time series data with periodic trends like daily temperatures or sales of seasonal products can be seen in periods fluctuating between highs and lows throughout the year. Generalized least squares estimators are often computed for such time series data as these estimators have minimum variance among all linear unbiased estimators. However, the generalized least squares solution can require extremely demanding computation when the data is large. This paper studies an efficient algorithm for generalized least squares estimation in periodic trended regression with autoregressive errors. We develop an algorithm that can substantially simplify generalized least squares computation by manipulating large sets of data into smaller sets. This is accomplished by coining a structured matrix for dimension reduction. Simulations show that the new computation methods using our algorithm can drastically reduce computing time. Our algorithm can be easily adapted to big data that show periodic trends often pertinent to economics, environmental studies, and engineering practices.

In strongly correlated oxides, heterostructures provide a powerful route to manipulate the charge, spin, orbital, and lattice degrees of freedom to create distinctive functionalities. In this work, we have achieved atomically precise interface control in YBa2Cu3O7−x/La0.7Ca0.3MnO3 (YBCO/LCMO) heterostructures and find a hidden effective doping. This mechanism is responsible for higher Tc in the sample with the MnO2-terminated interface than in that with the La0.7Ca0.3O-terminated interface. The MnO2-terminated sample also shows a larger magnetic moment of Mn together with a lower valence state. For more than a decade, the control of Tc in these heterostructures prior to this work has been solely via the variation of YBCO or LCMO thickness. This work hints at an alternative way of exploiting and exploring the interactions between superconductivity and magnetism in this system.

This paper develops trend estimation techniques for monthly maximum and minimum temperature time series observed in the 48 conterminous United States over the last century. While most scientists concur that this region has warmed on aggregate, there is no a priori reason to believe that temporal trends in extremes and averages will exhibit the same patterns. Indeed, under minor regularity conditions, the sample partial sum and maximum of stationary time series are asymptotically independent (statistically). Previous authors have suggested that minimum temperatures are warming faster than maximum temperatures in the United States; such an aspect can be investigated via the methods discussed in this study. Here, statistical models with extreme value and changepoint features are used to estimate trends and their standard errors. A spatial smoothing is then done to extract general structure. The results show that monthly maximum temperatures are not often greatly changing—perhaps surprisingly, there are many stations that show some cooling. In contrast, the minimum temperatures show significant warming. Overall, the southeastern United States shows the least warming (even some cooling), and the western United States, northern Midwest, and New England have experienced the most warming.

Prescribed fire is commonly applied world-wide as a tool for enhancing habitats and altering resource-selection patterns of grazing animals. A scientific basis for this practice has been established in some ecosystems but its efficacy has not been rigorously evaluated on mesic sagebrush steppe. Beginning in 2003, resource-selection patterns of beef cows were investigated using global positioning system (GPS) collars for 2 years before and for 5 years after a fall prescribed burn was applied to mesic sagebrush steppe in the Owyhee Mountains of southwestern Idaho, USA. Resource-selection functions (RSF) developed from these data indicated cattle selected for lightly to moderately burned areas for all 5 postfire years. Cattle had been neutral towards these areas prior to the fire when their distribution was primarily affected by slope, sagebrush dominance, and distance to upland water. Resource-selection responses to the fire lasted 2–3 years longer than would be expected for fire-induced, forage-quality improvement effects. Although this is a case study and caution should be taken in extrapolating these results, if applied under conditions similar to this study, livestock producers and natural resource managers can likely use fall prescribed fire in the mesic sagebrush steppe to affect cattle resource-use patterns for 5 years postfire.

When a straight line is fitted to time series data, generalized least squares (GLS) estimators of the trend slope and intercept are attractive as they are unbiased and of minimum variance. However, computing GLS estimators is laborious as their form depends on the autocovariances of the regression errors. On the other hand, ordinary least squares (OLS) estimators are easy to compute and do not involve the error autocovariance structure. It has been known for 50 years that OLS and GLS estimators have the same asymptotic variance when the errors are second-order stationary. Hence, little precision is gained by using GLS estimators in stationary error settings. This article revisits this classical issue, deriving explicit expressions for the GLS estimators and their variances when the regression errors are drawn from an autoregressive process. These expressions are used to show that OLS methods are even more efficient than previously thought. Specifically, we show that the convergence rate of variance differences is one polynomial degree higher than that of least squares estimator variances. We also refine Grenander's (1954) variance ratio. An example is presented where our new rates cannot be improved upon. Simulations show that the results change little when the autoregressive parameters are estimated.

Most of the long memory estimators for stationary fractionally integrated time series models are known to experience non-negligible bias in small and finite samples. Simple moment estimators are also vulnerable to such bias, but can easily be corrected. In this article, the authors propose bias reduction methods for a lag-one sample autocorrelation-based moment estimator. In order to reduce the bias of the moment estimator, the authors explicitly obtain the exact bias of lag-one sample autocorrelation up to the order n−1. An example where the exact first-order bias can be noticeably more accurate than its asymptotic counterpart, even for large samples, is presented. The authors show via a simulation study that the proposed methods are promising and effective in reducing the bias of the moment estimator with minimal variance inflation. The proposed methods are applied to the northern hemisphere data. The Canadian Journal of Statistics 37: 476–493; 2009 © 2009 Statistical Society of Canada Il est connu que la majorite des estimateurs a memoire longue pour les modeles de series chronologiques stationnaires a integration d'ordre fractionnaire possedent un biais non negligeable pour de petits echantillons. Les estimateurs obtenus par la methode des moments sont aussi vulnerables a un tel biais, mais celui-ci peut etre facilement corrige. Dans cet article, les auteurs proposent une methode de reduction du biais pour les estimateurs obtenus par la methode des moments bases sur l'autocorrelation echantillonnale de delai un. Dans le but de reduire le biais de l'estimateur par cette methode, les auteurs obtiennent de facon explicite la fonction d'autocorrelation echantillonnale d'ordre un avec une precision d'ordre n−1. Ils presentent aussi un exemple oI le biais exact du premier ordre est beaucoup plus precis que celui obtenu de facon asymptotique, et ce, meme pour de grands echantillons. a l'aide d'une etude de simulation, les auteurs montrent que les methodes proposees sont prometteuses et efficaces pour reduire le biais de l'estimateur par la methode des moments tout en ayant une augmentation minime de la variance. Les methodes proposees sont appliquees a des donnees sur l'hemisphere nord. La revue canadienne de statistique 37: 476–493; 2009 © 2009 Societe statistique du Canada

This article studies weighted, generalized, least squares estimators in simple linear regression with serially correlated errors. Closed-form expressions of weighted least squares estimators and variances are presented under some common stationary autocorrelation settings, a first-order autoregression and a first-order moving-average. These explicit expressions also have appealing applications, including an efficient weighted least squares computation method and a new sufficient and necessary condition on the equality of weighted least squares estimators and ordinary least squares estimators.

This paper proposes an accurate confidence interval for the trend parameter in a linear regression model with long memory errors. The interval is based upon an equivalent sum of squares method and is shown to perform comparably to a weighted least squares interval. The advantages of the proposed interval lies in its relative ease of computation and should be attractive to practitioners.

This article studies confidence intervals for regression parameters in time series settings. An equivalent sample size method is proposed that retains the simple interval structure inherent with white noise model errors, but modifies the sample size to account for the serial autocorrelations present in the errors. This makes the interval perform akin to weighted least squares intervals. The accuracy of the approach is demonstrated in three common regression problems. A noteworthy by-product of the work identifies explicit variances of several classical regression statistics in time series settings.

This paper studies properties of ordinary and generalised least squares estimators in a simple linear regression with stationary autocorrelated errors. Explicit expressions for the variances of the regression parameter estimators are derived for some common time series autocorrelation structures, including a first-order autoregression and general moving averages. Applications of the results include confidence intervals and an example where the variance of the trend slope estimator does not increase with increasing autocorrelation.

The total spectral weight \textit{S} of the emergent low-energy quasipaticles in high-temperature superconductors is explored by x-ray absorption spectroscopy. In order to examine the applicability of the Hubbard model, regimes that cover from zero doping to overdoping are investigated. In contrast to mean field theory, we found that \textit{S} deviates from linear dependence on the doping level \textit{p}. The slope of \textit{S} versus \textit{p} changes continuously throughout the whole doping range with no sign of saturation up to \textit{p} = 0.23. Therefore, the picture of Zhang-Rice singlet remains intact within the most prominent doping regimes of HTSC's., 5 pages, 3 figures

Single-phase ${\mathrm{La}}_{0.7}{\mathrm{Sn}}_{0.3}\mathrm{Mn}{\mathrm{O}}_{3}$ (LSnMO) thin films were fabricated on $\mathrm{La}\mathrm{Al}{\mathrm{O}}_{3}$ (LAO) substrates by pulsed laser deposition (PLD). The as-deposited films, though showed insulating characteristics with no sign of insulator-metal transition (IMT) down to very low temperatures, did display a paramagnetic-ferromagnetic transition (PFT) around $180\phantom{\rule{0.3em}{0ex}}\mathrm{K}$. The x-ray absorption spectroscopy (XAS) of the as-deposited LSnMO films shows signature of ${\mathrm{Mn}}^{3+}∕{\mathrm{Mn}}^{2+}$ mixed valence indicating that tetravalent Sn ions may have resulted in electron doping into the ${e}_{\mathrm{g}}$ band of Mn. The transmission electron microscopy (TEM) analyses on the as-deposited LSnMO films further confirmed that the films are epitaxial with uniform composition distributions. It is suggestive that the doping level of $x=0.3$ in ${\mathrm{La}}_{1\ensuremath{-}x}{\mathrm{Sn}}_{x}\mathrm{Mn}{\mathrm{O}}_{3}$ can be achieved without disrupting the perovskite structure or any composition inhomogeneity. On the other hand, ex situ annealing in oxygen as well as in argon atmosphere, though both drive the films to display IMT and a marked enhancement in the transition temperature, the preservation of LSnMO phase is somewhat doubtful. In the oxygen-annealing case, the evidence from the XAS measurements on Sn ions though showed the existence of tetravalent characteristics, the Hall measurements indicated that the obtained LSnMO films are $p$ type in nature. Furthermore, the TEM analyses also revealed the emergence of the Sn compounds, which may ultimately drive the obtained films into La-deficient ${\mathrm{La}}_{1\ensuremath{-}x}\mathrm{Mn}{\mathrm{O}}_{3}$ phases.

X-ray absorption spectroscopy (XAS), optical reflectance spectroscopy, and the Hall effect measurements were used to investigate the electronic structure in ${\mathrm{La}}_{0.7}{\mathrm{Ce}}_{0.3}\mathrm{Mn}{\mathrm{O}}_{3}$ thin films (LCeMO). The XAS results are consistent with those obtained from $\mathrm{LDA}+U$ calculations. In that the doping of Ce has shifted up the Fermi level and resulted in marked shrinkage of hole pockets originally existing in ${\mathrm{La}}_{0.7}{\mathrm{Ca}}_{0.3}\mathrm{Mn}{\mathrm{O}}_{3}$ (LCaMO). The Hall measurements indicate that in LCeMO the carriers are still displaying the characteristics of holes as $\mathrm{LDA}+U$ calculations predict. Analyses of the optical reflectance spectra evidently disapprove the scenario that the present LCeMO might have been dominated by the La-deficient phases.

In this paper, the photo-induced ultrafast thermoelastic dynamics of hexagonal HoMnO3 single crystals has been studied by using optical pump–probe spectroscopy. The thermoelastic effect in the ab-plane of hexagonal HoMnO3, associated with giant magnetoelastic coupling around the Neel temperature (TN), is intimately correlated to the emergence of a negative component in the transient reflectivity changes (ΔR/R) obtained in temperature-dependent pump–probe experiments. Moreover, the variation in the oscillation period in ΔR/R caused by a strain pulse propagating along the c-axis exhibits an abrupt drop around TN, presumably due to the antiferromagnetic-ordering-induced changes in ferroelectricity.