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Objetivou-se por este trabalho determinar o tamanho ótimo de parcela (top) para experimentos com mudas de mamoeiro ‘Golden THB’ a partir dos dados de avaliação de seis características das mudas produzidas em tubetes. As mudas foram obtidas na Caliman Agrícola S.A. e avaliadas no laboratório de Melhoramento de Plantas, no prédio de pós-graduação em Agricultura Tropical da Universidade Federal do Espírito Santo, no campus São Mateus. Foram avaliadas ao todo 384 mudas em quatro épocas do ano, sendo 96 mudas avaliadas em setembro de 2015, 96 em dezembro de 2015, 96 em março de 2016 e 96 em junho de 2016, sendo analisadas as seguintes características: altura da parte aérea, diâmetro do coleto, peso da matéria seca da parte aérea, da raiz e total e o índice de qualidade de Dickson. Foram utilizados os métodos da máxima curvatura modificado, em sua forma original segundo Meier & Lessman e com simulação bootstrap para determinar o top. Os métodos de Meier & Lessman e simulação bootstrap apresentaram resultados semelhantes entre si para cada característica, porém as características apresentaram top diferentes entre elas e entre as épocas de avaliação. Para contemplar experimentos com mudas de mamoeiro THB, em qualquer época do ano, avaliando-se apenas características não destrutivas são recomendadas cinco mudas por parcela e para avaliar características destrutivas, dez mudas por parcela.

Satellite sensors need to make a trade-off between revisit frequency and spatial resolution. This work presents a spatio-temporal image fusion method called Unpaired Spatio-Temporal Fusion of Image Patches (USTFIP). This method combines data from different multispectral sensors and creates images combining the best of each satellite in terms of frequency and resolution. It generates synthetic images and selects optimal information from cloud-contaminated images, to avoid the need of cloud-free matching pairs of satellite images. The removal of this restriction makes it easier to run our fusion algorithm even in the presence of clouds, which are frequent in time series of satellite images. The increasing demand of larger datasets makes necessary the use of computationally optimized methods. Therefore, this method is programmed to run in parallel reducing the run-time with regard to other methods. USTFIP is tested through an experimental scenario with similar procedures as Fit-FC, STARFM and FSDAF. Finally, USTFIP is the most robust, since its prediction accuracy deprecates at a much lower rate as classical requirements become progressively difficult to meet.

Wildland fires are one of the most important disturbances on Earth ecosystems, where the combination of remote sensing data and modern machine learning techniques offer a great potential for detection and monitoring of burned areas. Many algorithms have been developed in parallel to locate burned areas at local and global scales, but more accurate methodologies are still needed to provide precise and valuable information to climate users, environmentalists and public administrations. Gradient boosting-based methodologies have shown a good performance when dealing with segmentation of burned areas. In this paper, we propose a new method, called MBAGB, based on these methodologies for detecting and mapping burned areas in a multi-temporal setting of satellite imagery. We illustrate the procedure with the fires occurred in October 2017 in a region covering the North-Central Portugal and the North-West of Spain. Daily satellite images used for the definition of spectral indexes are taken from MODIS satellite products, between September and November 2017. The MBAGB accuracy metrics show the overall goodness of this method, and in particular, a very small number of false negatives in the identification of burned areas.

We propose an adaptive-sliding-window approach (LACPD) for the problem of change-point detection in a set of time-ordered observations. The proposed method is combined with sub-sampling techniques to compensate for the lack of enough data near the time series’ tails. Through a simulation study, we analyse its behaviour in the presence of an early/middle/late change-point in the mean, and compare its performance with some of the frequently used and recently developed change-point detection methods in terms of power, type I error probability, area under the ROC curves (AUC), absolute bias, variance, and root-mean-square error (RMSE). We conclude that LACPD outperforms other methods by maintaining a low type I error probability. Unlike some other methods, the performance of LACPD does not depend on the time index of change-points, and it generally has lower bias than other alternative methods. Moreover, in terms of variance and RMSE, it outperforms other methods when change-points are close to the time series’ tails, whereas it shows a similar (sometimes slightly poorer) performance as other methods when change-points are close to the middle of time series. Finally, we apply our proposal to two sets of real data: the well-known example of annual flow of the Nile river in Awsan, Egypt, from 1871 to 1970, and a novel remote sensing data application consisting of a 34-year time-series of satellite images of the Normalised Difference Vegetation Index in Wadi As-Sirham valley, Saudi Arabia, from 1986 to 2019. We conclude that LACPD shows a good performance in detecting the presence of a change as well as the time and magnitude of change in real conditions. This work has been supported by Project MTM2017-82553-R (AEI/ FEDER, UE), Project PID2020-113125RB-I00 (AEI) and the Caixa Foundation (ID1000010434), Caja Navarra Foundation, and UNED Pamplona, under Agreement LCF/PR/PR15/51100007.

The combination of freely accessible satellite imagery from multiple programs improves the spatio-temporal coverage of remote sensing data, but it exhibits barriers regarding the variety of web services, file formats, and data standards. Ris an open-source software environment with state-of-the-art statistical packages for the analysis of optical imagery. However, it lacks the tools for providing unified access to multi-program archives to customize and process the time series of images. This manuscript introduces RGISTools, a new software that solves these issues, and provides a working example on water mapping, which is a socially and environmentally relevant research field. The case study uses a digital elevation model and a rarely assessed combination of Landsat-8 and Sentinel-2 imagery to determine the water level of a reservoir in Northern Spain. The case study demonstrates how to acquire and process time series of surface reflectance data in an efficient manner. Our method achieves reasonably accurate results, with a root mean squared error of 0.90 m. Future improvements of the package involve the expansion of the workflow to cover the processing of radar images. This should counteract the limitation of the cloud coverage with multi-spectral images. This research was supported by the project MTM2017-82553-R(AEI/FEDER, UE). It has also received funding from the La Caixa Foundation (ID1000010434), the Caja Navarra Foundation, and UNED Pamplona, under Agreement LCF/PR/PR15/51100007.

Detecting change-points and trends are common tasks in the analysis of remote sensing data. Over the years, many different methods have been proposed for those purposes, including (modified) Mann-Kendall and Cox-Stuart tests for detecting trends; and Pettitt, Buishand range, Buishand U, standard normal homogeneity (Snh), Meanvar, structure change (Strucchange), breaks for additive season and trend (BFAST), and hierarchical divisive (E. divisive) for detecting change-points. In this paper, we describe a simulation study based on including different artificial, abrupt changes at different time-periods of image time series to assess the performances of such methods. The power of the test, type I error probability, and mean absolute error (MAE) were used as performance criteria, although MAE was only calculated for change-point detection methods. The study reveals that if the magnitude of change (or trend slope) is high, and/or the change does not occur in the first or last time-periods, the methods generally have a high power and a low MAE. However, in the presence of temporal autocorrelation, MAE raises, and the probability of introducing false positives increases noticeably. The modified versions of the Mann-Kendall method for autocorrelated data reduce/moderate its type I error probability, but this reduction comes with an important power diminution. In conclusion, taking a trade-off between the power of the test and type I error probability, we conclude that the original Mann-Kendall test is generally the preferable choice. Although Mann-Kendall is not able to identify the time-period of abrupt changes, it is more reliable than other methods when detecting the existence of such changes. Finally, we look for trend/change-points in land surface temperature (LST), day and night, via monthly MODIS images in Navarre, Spain, from January 2001 to December 2018. This work has been supported by Project MTM2017-82553-R (AEI/ FEDER, UE). It has also received funding from la Caixa Foundation (ID1000010434), Caja Navarra Foundation, and UNED Pamplona, under agreement LCF/PR/PR15/51100007.

There is a large number of data archives and web services offering free access to multispectral satellite imagery. Images from multiple sources are increasingly combined to improve the spatio-temporal coverage of measurements while achieving more accurate results. Archives and web services differ in their protocols, formats, and data standards, which are barriers to combine datasets. Here, we present RGISTools, an R package to create time-series of multispectral satellite images from multiple platforms in a harmonized and standardized way. We first provide an overview of the package functionalities, namely downloading, customizing, and processing multispectral satellite imagery for a region and time period of interest as well as a recent statistical method for gap-filling and smoothing series of images, called interpolation of the mean anomalies. We further show the capabilities of the package through a case study that combines Landsat-8 and Sentinel-2 satellite optical imagery to estimate the level of a water reservoir in Northern Spain. We expect RGISTools to foster research on data fusion and spatio-temporal modelling using satellite images from multiple programs., Comment: 31 pages, 6 figures

Objectives To evaluate the utility of virtual cystoscopy (VC) performed with CT urography in patients being studied under gross hematuria or patients being followed-up after a previous bladder cancer and compare the results with those obtained with gold standard technique (optical cystoscopy). Methods Retrospective study of 117 patients who were referred for VC by the Urology Department between May 2014 and May 2015. Those patients presented with gross hematuria or they were previously treated patients from bladder cancer being followed up. These patients were evaluated with MDCT and virtual cystoscopy after distending the bladder with air. The results were compared with those obtained with optical cystoscopy which was performed no more than a week after. Results The global sensitivity and specificity of VC were 81.8 and 92.1 per cent. Additional findings detected in CT urography were an aortic dissection, urinary lithiasis and colonic metastasis. Conclusion VC seems an useful technique in the diagnosis and follow-up for bladder cancer with a good correlation with OC. The main limitations are the impossibility of biopsy during the procedure and the detection of erythematous lesions. Collateral findings can be detected performed with CT urography although the high radiation exposure does not recommend their combined use.

When monitoring time series of remote sensing data, it is advisable to fill gaps, i.e., missing or distorted data, caused by atmospheric effects or technical failures. In this paper, a new method for filling these gaps called interpolation of the mean anomalies (IMA) is proposed and compared with some competitors. The method consists of: 1) defining a neighborhood for the target image from previous and subsequent images across previous and subsequent years; 2) computing the mean target image of the neighborhood; 3) estimating the anomalies in the target image by subtracting the mean image from the target image; 4) filtering the anomalies; 5) averaging the anomalies over a predefined window; 6) interpolating the averaged anomalies; and 7) adding the interpolated anomalies to the mean image. To assess the performance of the IMA method, both a real example and a simulation study are conducted with a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA and MODIS AQUA images captured over the region of Navarre (Spain) from 2011 to 2013. We analyze the land surface temperature (LST) day and night, and the normalized difference vegetation index (NDVI). In the simulation study, seven sizes of artificial clouds are randomly introduced to each image in the studied time series. The square root of the mean-squared prediction error (RMSE) between the original and the filled data is chosen as an indicator of the goodness of fit. The results show that the IMA method outperforms Timesat, Hants, and Gapfill (GF) in filling small, moderate, and big cloud gaps in both the day and night LST and NDVI data, reaching RMSE reductions of up to 23%. This work was supported in part by project MTM2017-82553-R (AEI/FEDER, UE), in part by projects PI015-2016 and PI043-2017 (Government of Navarra, Spain), and in part by the Fundación CAN-Obra Social Caixa-UNED Pamplona 2016 and 2017.

Outliers and missing data are commonly found in satellite imagery. These are usually caused by atmospheric or electronic failures, hampering the correct monitoring of remote-sensing data. To avoid distorted data, we propose a procedure called 'spatial functional prediction' (SFP). The SFP procedure consists of the following: (1) aggregating remote-sensing data for reducing the number of missing data and/or outliers; (2) additively decomposing the time series of images into a trend, a seasonal, and an error component; (3) defining the spatial functional data and predicting the trend component using an ordinary kriging; and (4) adding back the seasonal and error components to the predicted trend. The benefits of the SFP procedure are illustrated in the following scenarios: introducing random outliers, random missing data, mixtures of both, and artificial clouds in an extensive simulation study of composite images, and using daily images with real clouds. The following two derived variables are considered: land surface temperature (LST day) and normalized vegetation index (NDVI), which are obtained as remote-sensing data in a region in northern Spain during 2003–2016. The performance of SFP was checked using the root mean squared error (RMSE). A comparison with a procedure based on predicting with thin-plate splines (TpsP) is also made. We conclude that SFP is simpler and faster than TpsP, and provides smaller values of RMSE. This research was supported by Project MTM2017-82553-R (AEI/FEDER, UE), and by 'La Caixa' Foundation (ID 1000010434), Caja Navarra Foundation, and UNED Pamplona, under agreement LCF/PR/PR15/51100007.

Mortality counts are usually aggregated over age groups assuming similar effects of both time and region, yet the spatio-temporal evolution of cancer mortality rates may depend on changing age structures. In this paper, mortality rates are analyzed by region, time period and age group, and models including space-time, space-age, and age-time interactions are considered. The integrated nested Laplace approximation method, known as INLA, is adopted for model fitting and inference in order to reduce computing time in comparison with Markov chain Monte Carlo (McMC) methods. The methodology provides full posterior distributions of the quantities of interest while avoiding complex simulation techniques. The proposed models are used to analyze prostate cancer mortality data in 50 Spanish provinces over the period 1986-2010. The results reveal a decline in mortality since the late 1990s, particularly in the age group [65,70), probably because of the inclusion of the PSA (prostate-specific antigen) test and better treatment of early-stage disease. The decline is not clearly observed in the oldest age groups. Copyright © 2016 John Wiley & Sons, Ltd.

Satellite remote sensing data have become available in meteorology, agriculture, forestry, geology, regional planning, hydrology or natural environment sciences since several decades ago, because satellites provide routinely high quality images with different temporal and spatial resolutions. Joining, combining or smoothing these images for a better quality of information is a challenge not always properly solved. In this regard, geostatistics, as the spatiotemporal stochastic techniques of georeferenced data, is a very helpful and powerful tool not enough explored in this area yet. Here, we analyze the current use of some of the geostatistical tools in satellite image analysis, and provide an introduction to this subject for potential researchers. This research was supported by the Spanish Ministry of Economy, Industry and Competitiveness (Project MTM2017-82553-R), the Government of Navarra (Project PI015, 2016 and Project PI043 2017), and by the Fundación Caja Navarra-UNED Pamplona (2016 and 2017).

Multitemporal imagery is by and large geometrically and radiometrically accurate, but the residual noise arising from removal clouds and other atmospheric and electronic effects can produce outliers that must be mitigated to properly exploit the remote sensing information. In this study, we show how ground-truth data from rain gauge stations can improve the quality of satellite imagery. To this end, a simulation study is conducted wherein different sizes of outlier outbreaks are spread and randomly introduced in the normalized difference vegetation index (NDVI) and the day and night land surface temperature (LST) of composite images from Navarre (Spain) between 2011 and 2015. To remove outliers, a new method called thin-plate splines with covariates (TpsWc) is proposed. This method consists of smoothing the median anomalies with a thin-plate spline model, whereby transformed ground-truth data are the external covariates of the model. The performance of the proposed method is measured with the square root of the mean square error (RMSE), calculated as the root of the pixel-by-pixel mean square differences between the original data and the predicted data with the TpsWc model and with a state-space model with and without covariates. The study shows that the use of ground-truth data reduces the RMSE in both the TpsWc model and the state-space model used for comparison purposes. The new method successfully removes the abnormal data while preserving the phenology of the raw data. The RMSE reduction percentage varies according to the derived variables (NDVI or LST), but reductions of up to 20% are achieved with the new proposal. This research was supported by the Spanish Ministry of Economy, Industry and Competitiveness (project MTM2017-82553-R) jointly financed with the European Regional Development Fund (FEDER), the Government of Navarre (PI015-2016 and PI043-2017 projects) and the Fundación CAN-Obra Social Caixa-UNED Pamplona 2016 and 2017.

The free access to satellite images since more than 40 years ago has provoked a rapid increase of multitemporal derived information of remote sensing data that should be summarized and analyzed for future inferences. In particular, the study of trends and trend changes is of crucial interest in many studies of phenology, climatology, agriculture, hydrology, geology or many other environmental disciplines. Overall, the normalized difference vegetation index (NDVI), as a satellite derived variable, plays a crucial role because of its usefulness for vegetation and landscape characterization, land use and land cover mapping, environmental monitoring, climate change or crop prediction models. Since the eighties, it can be retrieved all over the world from different satellites. In this work we propose to analyze its temporal evolution, looking for breakpoints or change-points in trends of the surfaces occupied by four NDVI classifications made in Spain from 1981 to 2015. The results show a decrease of bare soils and semi-bare soils starting in the middle nineties or before, and a slight increase of middle-vegetation and high-vegetation soils starting in 1990 and 2000 respectively.

Summary Knowledge of the current state of the art in information and communication technology of businesses (ICTB) is an important issue for governments, markets and policy makers, because information technology improves access to information and plays an important role in firms’ competitiveness. Statistical agencies use normalized surveys to provide harmonized statistics about the use of technology in enterprises. Classical design-based estimators are appropriate for large domains, because direct estimates are consistent and easy to obtain by using sampling weights. However, to supply estimates for unplanned domains, where the sample size is random, model-based estimators are usually required. In this paper, alternative logistic model-based estimators are suggested to derive small area estimates from ICTB surveys. Final estimates are benchmarked to achieve coherence with direct estimates in larger domains, and standard errors are given by using bootstrap techniques. A Monte Carlo simulation study is conducted to compare the performance of the small area estimators proposed and to evaluate the behaviour of the mean-squared error estimator. Results are illustrated with the 2010 ICTB survey of the Basque country (Spain).

In this paper age–space–time models based on one and two-dimensional P-splines with B-spline bases are proposed for smoothing mortality rates, where both fixed relative scale and scale invariant two-dimensional penalties are examined. Model fitting and inference are carried out using integrated nested Laplace approximations, a recent Bayesian technique that speeds up computations compared to McMC methods. The models will be illustrated with Spanish breast cancer mortality data during the period 1985–2010, where a general decline in breast cancer mortality has been observed in Spanish provinces in the last decades. The results reveal that mortality rates for the oldest age groups do not decrease in all provinces.

The normalized difference vegetation index (NDVI) is an important indicator for evaluating vegetation change, monitoring land surface fluxes or predicting crop models. Due to the great availability of images provided by different satellites in recent years, much attention has been devoted to testing trend changes with a time series of NDVI individual pixels. However, the spatial dependence inherent in these data is usually lost unless global scales are analyzed. In this paper, we propose incorporating both the spatial and the temporal dependence among pixels using a stochastic spatio-temporal model for estimating the NDVI distribution thoroughly. The stochastic model is a state-space model that uses meteorological data of the Climatic Research Unit (CRU TS3.10) as auxiliary information. The model will be estimated with the Expectation-Maximization (EM) algorithm. The result is a set of smoothed images providing an overall analysis of the NDVI distribution across space and time, where fluctuations generated by atmospheric disturbances, fire events, land-use/cover changes or engineering problems from image capture are treated as random fluctuations. The illustration is carried out with the third generation of NDVI images, termed NDVI3g, of the Global Inventory Modeling and Mapping Studies (GIMMS) in continental Spain. This data are taken in bymonthly periods from January 2011 to December 2013, but the model can be applied to many other variables, countries or regions with different resolutions. This research was supported by the Spanish Ministry of Economy and Competitiveness (Project MTM2014-51992-R), the Government of Navarre (Project PI015, 2016), and by the Fundación Caja Navarra-UNED Pamplona (2016).

Accumulated daily rainfall in non-observed locations on a particular day is frequently required as input to decision-making tools in precision agriculture or for hydrological or meteorological studies. Various solutions and estimation procedures have been proposed in the literature depending on the auxiliary information and the availability of data, but most such solutions are oriented to interpolating spatial data without incorporating temporal dependence. When data are available in space and time, spatiotemporal models usually provide better solutions. Here, we analyse the performance of three spatiotemporal models fitted to the whole sampled set and to clusters within the sampled set. The data consists of daily observations collected from 87 manual rainfall gauges from 1990 to 2010 in Navarre, Spain. The accuracy and precision of the interpolated data are compared with real data from 33 automated rainfall gauges in the same region, but placed in different locations than the manual rainfall gauges. Root mean squared error by months and by year are also provided. To illustrate these models, we also map interpolated daily precipitations and standard errors on a 1 km2 grid in the whole region.

Spatio-temporal disease mapping comprises a wide range of models used to describe the distribution of a disease in space and its evolution in time. These models have been commonly formulated within a hierarchical Bayesian framework with two main approaches: an empirical Bayes (EB) and a fully Bayes (FB) approach. The EB approach provides point estimates of the parameters relying on the well-known penalized quasi-likelihood (PQL) technique. The FB approach provides the posterior distribution of the target parameters. These marginal distributions are not usually available in closed form and common estimation procedures are based on Markov chain Monte Carlo (MCMC) methods. However, the spatio-temporal models used in disease mapping are often very complex and MCMC methods may lead to large Monte Carlo errors and a huge computation time if the dimension of the data at hand is large. To circumvent these potential inconveniences, a new technique called integrated nested Laplace approximations (INLA), based on nested Laplace approximations, has been proposed for Bayesian inference in latent Gaussian models. In this paper, we show how to fit different spatio-temporal models for disease mapping with INLA using the Leroux CAR prior for the spatial component, and we compare it with PQL via a simulation study. The spatio-temporal distribution of male brain cancer mortality in Spain during the period 1986–2010 is also analysed.

Current cancer mortality data are available with a delay of roughly three years due to the administrative procedure necessary to create the registries. Therefore, health agencies rely on forecast cancer deaths. In this context, statistical procedures providing mortality/incidence risk predictions for different regions or health areas are very useful. These predictions are essential for defining priorities for cancer prevention and treatment. The main objective of this work is to evaluate the predictive performance of alternative spatio-temporal models for short-term cancer risk/counts prediction in small areas. All the models analyzed here are presented under a general-mixed model framework, providing a unified structure of presentation and facilitating the use of similar tools for computing the prediction mean squared error. Prostate cancer mortality data are used to illustrate the behavior of the different models in Spanish provinces.

Spatio–temporal statistical models have been proposed for the analysis of the temporal evolution of the geographical pattern of mortality (or incidence) risks in disease mapping. However, as far as we know, functional approaches based on Hilbert-valued processes have not been used so far in this area. In this paper, the autoregressive Hilbertian process framework is adopted to estimate the functional temporal evolution of mortality relative risk maps. Specifically, the penalized functional estimation of log-relative risk maps is considered to smooth the classical standardized mortality ratio. The reproducing kernel Hilbert space (RKHS) norm is selected for definition of the penalty term. This RKHS-based approach is combined with the Kalman filtering algorithm for the spatio–temporal estimation of risk. Functional confidence intervals are also derived for detecting high risk areas. The proposed methodology is illustrated analyzing breast cancer mortality data in the provinces of Spain during the period 1975–2005. A simulation study is performed to compare the ARH(1) based estimation with the classical spatio–temporal conditional autoregressive approach.

Nowadays, GPS receivers are very reliable because of their good accuracy and precision; however, uncertainty is also inherent in geospatial data. Quality of GPS measurements can be influenced by atmospheric disturbances, multipathing, synchronization of clocks, satellite geometry, geographical features of the observed region, low broadcasting coverage, inadequate transmitting formats, or human or instrumental unknown errors. Assuming that the scenario and technical conditions that can influence the quality of GPS measurements are optimal, that functional and stochastic models that process the signals to a geodetic measurement are correct, and that all the GPS observables are taken in the same conditions, it is still possible to estimate the positional errors as the difference between the real coordinates and those measured by the GPS. In this paper, three spatial linear mixed models, one for each axis, are used for modelling real-time kinematic GPS accuracy and precision, of a multiple-reference-station network in dual-frequency with carrier phase measurements. Along the paper, the proposed models provide an estimate of the “accuracy” in terms of bias defined as the difference between real coordinates and measured coordinates after being processed and “precision” through the standard errors of the estimated differences. This is done using ten different transmitting formats. Mapping and quantifying these differences can be interesting for users and GPS professionals. The performance of these models is illustrated by mapping positional error estimates within the whole region of Navarre, Spain. Sampled data have been taken in 54 out of the 211 geodetic vertex points of this region. Maps show interesting error patterns depending on transmitting formats, the different axes, and the geographical characteristics of the region. Higher differences are found in regions with bad broadcasting coverage, due to the presence of mountains and high degree of humidity.

To evaluate the utility of virtual cystoscopy (VC) performed with CT urography in patients being studied under gross hematuria or patients being followed-up after a previous bladder cancer and compare the results with those obtained with gold standard technique (optical cystoscopy).Retrospective study of 117 patients who were referred for VC by the Urology Department between May 2014 and May 2015. Those patients presented with gross hematuria or they were previously treated patients from bladder cancer being followed up. These patients were evaluated with MDCT and virtual cystoscopy after distending the bladder with air. The results were compared with those obtained with optical cystoscopy which was performed no more than a week after.The global sensitivity and specificity of VC were 81,8 and 92,1%. Aditional findings detected in CT urography were an aortic dissection, urinary lithiasis and colonic metastasis.VC seems an useful technique in the diagnosis and follow-up for bladder cancer with a good correlation with OC. The main limitations are the impossibility of biopsy during the procedure and the detection of erythematous lesions. Collateral findings can be detected performed with CT urography although the high radiation exposure does not recommend their combined use.

In some diseases it is well-known that a unimodal mortality pattern exists. A clear example in developed countries is breast cancer, where mortality increased sharply until the nineties and then decreased. This clear unimodal pattern is not necessarily applicable to all regions within a country. In this paper, we develop statistical tools to check if the unimodality pattern persists within regions using order restricted inference. Break points as well as con dence intervals are also provided. In addition, a new test for checking monotonicity against unimodality is derived allowing to discriminate between a simple increasing pattern and an up-then-down response pattern. A comparison with the widely used joinpoint regression technique under unimodality is provided. We show that the joinpoint technique could fail when the underlying function is not piecewise linear. Results will be illustrated using age-speci c breast cancer mortality data from Spain in the period 1975-2005., Ministerio de Ciencia e Innovación grant (MTM2012-37129), Spanish Ministry of Science and Innovation project MTM 2011-22664, Ministerio de Ciencia e Innovación and FEDER grant MTM2014-51992-R, Health Department of Navarre Government (Project 113, Res. 2186/2014)

Smoothing risks is one of the main goals in disease mapping as classical measures, such as standardized mortality ratios, can be extremely variable. However, smoothing risks might hinder the detection of high risk areas, since these two objectives are somewhat contradictory. Most of the work on smoothing risks and detection of high risk areas has been derived using conditional autoregressive (CAR) models. In this work, penalized splines (P-splines) models are also investigated. Confidence intervals for the log-relative risk predictor will be derived as a tool to detect high-risk areas. The performance of P-spline and CAR models will be compared in terms of smoothing (relative bias), sensitivity (ability to detect high risk areas), and specificity (ability to discard false patterns created by noise) through a simulation study based on the well-known Scottish lip cancer data.

One of the main objectives in disease mapping is the identification of temporal trends and the production of a series of smoothed maps from which spatial patterns of mortality risks can be monitored over time. When studying rare diseases, conditional autoregressive models have been commonly used for smoothing risks. In this work, a P-spline ANOVA type model is used instead. The model is anisotropic and explicitly considers different smooth terms for space, time, and space-time interaction avoiding, in addition, model identifiability problems. The mean squared error of the log-risk predictor is derived accounting for the variability associated to the estimation of the smoothing parameters. The procedure is illustrated analyzing Spanish prostate cancer mortality data in the period 1975–2008.

Summary Current demand for accountability and efficiency of healthcare organizations, combined with the greater availability of routine data on clinical care and outcomes, has led to an increased focus on statistical methods in healthcare regulation. We consider three different regulatory functions in which statistical analysis plays a vital role: rating organizations, deciding whom to inspect and continuous surveillance for arising problems. A common approach to data standardization based on (possibly overdispersed) Z-scores is proposed, although specific tools are used for assessing performance against a target, combining indicators when screening for inspection, and continuous monitoring using risk-adjusted sequential testing procedures. We pay particular attention to the problem of simultaneously monitoring over 200000 indicators for excess mortality, both with respect to the statistical issues surrounding massive multiplicity, and the organizational aspects of dealing with such a complex but high profile process.

Data on disease incidence or mortality over a set of contiguous regions have been commonly used to describe geographic patterns of disease, helping epidemiologists and public health researchers to identify possible etiologic factors. Nowadays, the availability of historical mortality registers offers the possibility of going further, describing the spatio-temporal distribution of risks. The literature on spatio-temporal modeling of risks is very rich, and it is mainly focused on the use of conditional autoregressive models from a fully Bayesian perspective. The complexity of the estimation procedure makes the Empirical Bayes approach a plausible alternative. In this context, it is of interest to test for interaction between space and time, as an absence of space–time interactions simplifies modeling and interpretation. In this work, a score test is derived as well as a bootstrap approximation of its null distribution. A parametric bootstrap test is also provided for comparison purposes. Results are illustrated using brain cancer mortality data from Spain in the period 1996–2005. Copyright © 2011 John Wiley & Sons, Ltd.

In recent decades, a decline in breast cancer mortality has been observed across Europe, and also in Spain. Our objective is to assess the spatio-temporal pattern during the period 1975-2005 by specific age groups (45, 45-64, ≥65) in the Spanish provinces.For each age group, a spatio-temporal P-spline model with a B-spline basis is used to smooth the mortality risks. Smoothing is carried out in three dimensions: longitude, latitude, and time, allowing for a different time evolution of both spatial components. The age-specific decline is calculated as the maximum of the estimated curve in each province. A confidence band for each curve is also provided.For the first age group (45), the decline in the different provinces is observed between 1986 and 1991. For women aged between 45 to 64 years, the change occurs between 1990 and 1993. For the third age group (≥65), change points range from 1992 to 2000, unlike Malaga and Cadiz where the change has not been observed in the studied period. Northern and some Mediterranean provinces are the areas with higher mortality risks for all the age groups.A different behavior for breast cancer mortality risks is observed for different provinces among the age specific groups. The decline of mortality is delayed for the oldest age group. Province differences in the implementation of screening programs could explain some of the observed differences.

Nine serological types of Avian Paramyxovirus (APMV) have been recognized. Newcastle Disease Virus (APMV-1) is the most extensively characterized virus; while relatively little information is available for the other APMV serotypes. In the present study, we examined the pathogenicity of two strains of APMV-2, Yucaipa and Bangor, in 9-day-old embryonated chicken eggs, 1-day-old Specific Pathogen Free (SPF) chicks, and 4-week-old SPF chickens and turkeys. The mean death time (MDT) in 9-day-old embryonated chicken eggs was more than 168 h for both strains and their Intra Cerebral Pathogenicity Index (ICPI) was zero, indicating that these viruses are nonpathogenic in chickens. When inoculated intracerebrally in 1-day old chicks, neither strain caused disease nor replicated detectably in the brain. This suggests that the zero ICPI value of APMV-2 reflects the inability of the virus to grow in neural cells. Groups of twelve 4-week-old SPF chickens and turkeys were inoculated oculonasally with either strain, and three birds per group were euthanized on days 2, 4, 6, and 14 post-inoculation for analysis. There were no overt clinical signs of illnesses, although all birds seroconverted by day 6. The viruses were isolated predominantly from the respiratory and alimentary tracts. Immunohistochemistry studies also showed the presence of large amount of viral antigens in epithelial linings of respiratory and alimentary tracts. There also was evidence of systemic spread even though the cleavage site of the viral fusion glycoprotein does not contain the canonical furin protease cleavage site.

Measuring forest tree biomass is becoming a very important issue due to the general environmental awareness motivated by global warming and climate change. However, weighing a tree is a very complicated, expensive, and destructive process. The tree is divided into several parts, and the total weight is obtained by adding the weight of the different components. The biomass information of a forest is obtained using statistical models, but one of the main difficulties is that the additivity property is not generally satisfied, i.e., when adding the predicted weights for the different tree components, the result does not match up with the total weight predicted for the tree. In this work, alternative methods for obtaining biomass predictions satisfying the additivity property are analyzed. In particular, segmented regression models with a common break point and penalized splines with the same smoothing parameter achieve the additivity property without any further adjustments. Some classical models will be also used for comparison purposes. Results are illustrated with real data from a beech forest (European project FORSEE-020) in the province of Navarre, Spain.

In the last few decades there has been an improvement in the statistical methods used to display the geographical patterns of mortality and disease incidence. These methods consider spatial models to smooth the classical standardized mortality ratio (SMR). Nowadays, interest relies on extending these spatial models to incorporate time trends and spatio-temporal interactions due to the availability of historical high quality mortality registers recorded during the last 20 years. In this work, alternative Bayesian spatio-temporal models are fitted using MCMC techniques. The performance of these models is analyzed through a simulation study with two objectives in mind: the first one is to evaluate the relative bias and relative standard error of the posterior mean relative risks, and the second one is to investigate recent Bayesian decision rules to detect raised-risk areas in a spatio-temporal context. The simulation study is based on mortality data due to colorectal cancer in males from Navarra, Spain, corresponding to four 5-year time windows. When there are a number of high-risk areas in some of the time periods we conclude that the bias of the posterior mean relative risks could be substantial. The decision rules to detect these high-risk areas should be determined with caution. A final rule combining alternative threshold and cutoff values for the different time periods seems to be needed. Copyright © 2009 John Wiley & Sons, Ltd.

Semiparametric models combining both non-parametric trends and small area random effects are now currently being investigated in small area estimation (SAE). These models can prevent bias when the functional form of the relationship between the response and the covariates is unknown. Furthermore, penalized spline regression can be a good tool to incorporate non-parametric regression models into the SAE techniques, as it can be represented as a mixed effects model. A penalized spline model is considered to analyze trends in small areas and to forecast future values of the response. The prediction mean squared error (MSE) for the fitted and the predicted values, together with estimators for those quantities, are derived. The procedure is illustrated with real data consisting of average prices per squared meter of used dwellings in nine neighborhoods of the city of Vitoria, Spain, during the period 1993-2007. Dwelling prices for the next five years are also forecast. A simulation study is conducted to assess the performance of both the small area trend estimator and the prediction MSE estimators. The results confirm a good behavior of the proposed estimators in terms of bias and variability.

Analyzing the temporal evolution of the geographical distribution of mortality (or incidence) risks is an important area of research in disease mapping. It might help to better determining the risk factors involved in the studied disease, and to address important epidemiological questions about the stability of the estimated patterns of disease. Traditionally, risk smoothing is carried out using conditional autoregressive (CAR) models but very recently, penalized splines have also been considered in an Empirical Bayes (EB) spatial context to estimate large-scale spatial trends together with region random effects. In this paper, penalized splines for smoothing risks in both the spatial and the temporal dimensions will be applied. The mean squared error (MSE) of the log-risk predictor will be derived allowing for constructing confidence intervals for the risks. To illustrate the procedure mortality data due to brain cancer in continental Spain over the period 1996–;2005 are analyzed. Copyright © 2009 John Wiley & Sons, Ltd.

Disease mapping studies have experienced an enormous development in the last twenty years. Both an Empirical Bayes (EB) and a Fully Bayes (FB) approach have been used for smoothing purposes. However, an excess of smoothing might hinder the detection of true high-risk areas. Identifying these extreme regions minimizing the misclassification of background or normal areas, and then, avoiding false alarms is crucial in epidemiology. Bayesian decision rules, based on the posterior distribution of the relative risks, have been investigated for this task, but no similar studies have been conducted under the EB approach. Within this framework, second order correct estimators of the MSE of the log-relative risk predictor can be used to build appropriate confidence intervals for the relative risks. Their ability to detect high-risk areas is investigated through a simulation study using the geographical structure of the well-known Scottish lip cancer data. Bayesian credibility intervals and decision rules, based on the posterior distribution of the relative risks, are also investigated to check if any of the approaches outperforms the others when classifying high-risk regions. The conclusion is that Bayesian decision rules, exploiting the posterior distribution of the relative risks, are more powerful to detect high-risk areas than EB confidence intervals, but no general rules can be defined as a global criterion to be routinely applied in every real setting.

Statistics for small areas within larger regions are recently required for many economic variables. However, when adding the estimates of the small areas within the larger regions, the results do not match up to those obtained with the appropriate estimator originally derived for the larger region. To avoid discrepancies between estimates benchmarking methods are commonly used in practice. In this paper, we discuss the suitability of using a restricted predictor versus a traditional direct calibrated estimator. The results are illustrated with the 2000 Business Survey of the Basque Country, Spain.

Linear mixed models have been frequently used to provide estimates in small areas. However, when aggregating small areas within the same region, the sum of these small area estimates does not generally match up with the estimate obtained using an appropriate estimator for the larger region. Then, benchmarking the model-dependent estimates to the ones obtained at certain level of aggregation is needed. In this paper, we propose a small area estimator based on a linear mixed effects model with restrictions to guarantee the concordance between the aggregations of small area estimates and those reported by statistical agencies for larger domains using a synthetic estimator. The mean squared prediction error of the restricted estimator is also derived and its performance is evaluated through a simulation study. The procedure is applied to the 2002 Business Survey of the Basque Country, Spain.

Mapping of small area mortality (or incidence) risks is a widely used technique in public health research. The standardized mortality ratio (SMR) is a common direct index of disease mortality, but it might be very inaccurate in areas or counties with low population. Hence, the use of models that borrow information from related (neighbouring) regions, smoothing the crude mortality risks, is recommended. In this work, we focus on comparing prediction error estimators used in Empirical Bayes (EB) disease mapping, which are correct up to first order, with alternative first and second-order correct proposals from the small area estimation literature. Our main interest is to check if second-order approximations of the prediction error improve confidence intervals for the relative risks. The well known Scottish lip cancer data is used for illustrative purposes. A simulation study is also conducted indicating that there is not much gain in efficiency when using second-order corrections. Copyright © 2007 John Wiley & Sons, Ltd.

Nowadays, the detection of areas with high nitrate concentration in groundwater is of public concern because an excess of these nitrogen compounds in any area represents serious environmental pollution. Hence, governmental agencies require geostatistical researchers to develop methods for assessing nitrate concentration. In this paper, we propose a linear mixed model which incorporates both spatial as well as longitudinal information with the particular objective of detecting areas with excess of nitrate in groundwater. The model extends in a natural way the traditional kriging approach, incorporating the longitudinal dimension. We illustrate the results using real data taken from wells pouring into the Ebro river and its tributaries in the province of Navarra, Spain.

Model-based estimators are becoming very popular in statistical offices because Governments require accurate estimates for small domains that were not planned when the study was designed, as their inclusion would have produced an increase in the cost of the study. The sample sizes in these domains are very small or even zero; consequently, traditional direct design-based estimators lead to unacceptably large standard errors. In this regard, model-based estimators that ‘borrow information’ from related areas by using auxiliary information are appropriate. This paper reviews, under the model-based approach, a BLUP synthetic and an EBLUP estimator. The goal is to obtain estimators of domain totals when there are several domains with very small sample sizes or without sampled units. We also provide detailed expressions of the mean squared error at different levels of aggregation. The results are illustrated with real data from the Basque Country Business Survey.

Designed for an intermediate undergraduate course, Probability and Statistics with R shows students how to solve various statistical problems using both parametric and nonparametric techniques via the open source software R. It provides numerous real-world examples, carefully explained proofs, end-of-chapter problems, and illuminating graphs to facilitate hands-on learning. Integrating theory with practice, the text briefly introduces the syntax, structures, and functions of the S language, before covering important graphically and numerically descriptive methods. The next several chapters elucidate probability and random variables topics, including univariate and multivariate distributions. After exploring sampling distributions, the authors discuss point estimation, confidence intervals, hypothesis testing, and a wide range of nonparametric methods. With a focus on experimental design, the book also presents fixed- and random-effects models as well as randomized block and two-factor factorial designs. The final chapter describes simple and multiple regression analyses. Demonstrating that R can be used as a powerful teaching aid, this comprehensive text presents extensive treatments of data analysis using parametric and nonparametric techniques. It effectively links statistical concepts with R procedures, enabling the application of the language to the vast world of statistics.

Extrapolating cancer mortality trends can be very valuable as a tool to predict cancer burden. National Health Agencies use different models to figure out future evolution of cancer, but they mainly work at national level. However, developed countries are divided into different regions with their own governments and health care systems, and this should be taken into account. In this paper, an ANOVA-type P-spline model is considered to predict the number of mortality cases in forthcoming years in regions within a country. The model is very interesting as it allows to split the predictions into components representing region-specific features and characteristics common to the whole country. Prediction variability is also calculated to provide prediction intervals. Real data on cancer mortality are used for illustration., REVSTAT-Statistical Journal, Vol. 13 No. 1 (2015): REVSTAT-Statistical Journal

Mortality counts are usually aggregated over age groups assuming similar effects of both time and region, yet the spatio-temporal evolution of cancer mortality rates may depend on changing age structures. In this paper, mortality rates are analyzed by region, time period and age group, and models including space-time, space-age, and age-time interactions are considered. The integrated nested Laplace approximation method, known as INLA, is adopted for model fitting and inference in order to reduce computing time in comparison with Markov chain Monte Carlo (McMC) methods. The methodology provides full posterior distributions of the quantities of interest while avoiding complex simulation techniques. The proposed models are used to analyze prostate cancer mortality data in 50 Spanish provinces over the period 1986-2010. The results reveal a decline in mortality since the late 1990s, particularly in the age group [65,70), probably because of the inclusion of the PSA (prostate-specific antigen) test and better treatment of early-stage disease. The decline is not clearly observed in the oldest age groups. Copyright © 2016 John WileySons, Ltd.