Resampling Approach Research Articles

Interval-based, nonparametric approach for resampling of fuzzy numbers

In this paper, we propose two new nonparametric resampling methods for the simulation of bootstrap-like samples of fuzzy numbers. The generated secondary samples are based on an input set (i.e., a primary sample) consisting of left–right fuzzy numbers. The proposed approaches utilize random simulations in a way which, to some extent, resembles a bootstrap. However, contrary to the classical bootstrap approach, the proposed methods are based on alpha-cuts of fuzzy numbers, which are generated in a new nonparametric way. Therefore, these procedures give us an opportunity to create ”not exactly the same as previous” fuzzy numbers and also lead to greater diversity of the obtained output. Moreover, we check whether the introduced methods can be successfully applied in two statistical tests about the mean value of a population of fuzzy numbers.

A method for combining p-values in meta-analysis by gamma distributions

ABSTRACTCombining p-values from statistical tests across different studies is the most commonly used approach in meta-analysis for evolutionary biology. The most commonly used p-value combination methods mainly incorporate the z-transform tests (e.g., the un-weighted z-test and the weighted z-test) and the gamma-transform tests (e.g., the CZ method [Z. Chen, W. Yang, Q. Liu, J.Y. Yang, J. Li, and M.Q. Yang, A new statistical approach to combining p-values using gamma distribution and its application to genomewide association study, Bioinformatics 15 (2014), p. S3]). However, among these existing p-value combination methods, no method is uniformly most powerful in all situations [Chen et al. 2014]. In this paper, we propose a meta-analysis method based on the gamma distribution, MAGD, by pooling the p-values from independent studies. The newly proposed test, MAGD, allows for flexible accommodating of the different levels of heterogeneity of effect sizes across individual studies. The MAGD simultaneously retains all the characters of the z-transform tests and the gamma-transform tests. We also propose an easy-to-implement resampling approach for estimating the empirical p-values of MAGD for the finite sample size. Simulation studies and two data applications show that the proposed method MAGD is essentially as powerful as the z-transform tests (the gamma-transform tests) under the circumstance with the homogeneous (heterogeneous) effect sizes across studies.

Ensemble outlier detection and gene selection in triple-negative breast cancer data

BackgroundLearning accurate models from ‘omics data is bringing many challenges due to their inherent high-dimensionality, e.g. the number of gene expression variables, and comparatively lower sample sizes, which leads to ill-posed inverse problems. Furthermore, the presence of outliers, either experimental errors or interesting abnormal clinical cases, may severely hamper a correct classification of patients and the identification of reliable biomarkers for a particular disease. We propose to address this problem through an ensemble classification setting based on distinct feature selection and modeling strategies, including logistic regression with elastic net regularization, Sparse Partial Least Squares - Discriminant Analysis (SPLS-DA) and Sparse Generalized PLS (SGPLS), coupled with an evaluation of the individuals’ outlierness based on the Cook’s distance. The consensus is achieved with the Rank Product statistics corrected for multiple testing, which gives a final list of sorted observations by their outlierness level.ResultsWe applied this strategy for the classification of Triple-Negative Breast Cancer (TNBC) RNA-Seq and clinical data from the Cancer Genome Atlas (TCGA). The detected 24 outliers were identified as putative mislabeled samples, corresponding to individuals with discrepant clinical labels for the HER2 receptor, but also individuals with abnormal expression values of ER, PR and HER2, contradictory with the corresponding clinical labels, which may invalidate the initial TNBC label. Moreover, the model consensus approach leads to the selection of a set of genes that may be linked to the disease. These results are robust to a resampling approach, either by selecting a subset of patients or a subset of genes, with a significant overlap of the outlier patients identified.ConclusionsThe proposed ensemble outlier detection approach constitutes a robust procedure to identify abnormal cases and consensus covariates, which may improve biomarker selection for precision medicine applications. The method can also be easily extended to other regression models and datasets.

Using repeated 137Cs and 210Pbex measurements to establish sediment budgets for different time windows and explore the effect of connectivity on soil erosion rates in a small experimental catchment in Southern Italy

AbstractOver the last 50 years, different approaches have been employed in order to predict environmental risk due to soil erosion and to propose effective strategies to control erosion and sedimentation. The use of fallout radionuclides, mainly caesium‐137 (137Cs) and excess lead‐210 (210Pbex), has proved to be a very effective complement to existing traditional methods, especially if a resampling strategy is employed. The resampling approach involves repeating soil sampling and radionuclide measurements within the same site, after a known amount of time (generally 10 to 15 years). This strategy, in the light of the possibility to obtain independent estimates of soil erosion for different periods, makes it possible to establish sediment budgets for different time windows. The use of sediment budgets represents an important tool for providing information on catchment behaviour regarding the relationship between the sediment output at the catchment outlet and rates of sediment redistribution within the catchment. For this reason, this approach can provide important indications of possible changes of sediment connectivity at the catchment scale. The study reported here focuses on the W1 catchment (1.47 ha), located in Calabria, Southern Italy, for which long‐term measurements of sediment yield are also available. The W1 catchment supports a rangeland vegetation cover, and no change in land use occurred during the last 100 years. The catchment was sampled for 137Cs and 210Pbex analyses in 2001 and resampled in 2014 to provide estimates of soil erosion and deposition for different time windows. The uncertainty associated with these estimates is also discussed, and some guidelines for the application of 210Pbex as a soil erosion tracer are provided. The results provided by the measurements of sediment yield at the catchment outlet provide evidence of a general increase of soil erosion rates during the last 15–20 years. The estimates of sediment redistribution provided by the measurements of 210Pbex demonstrated that these results reflect an increase in the sediment delivery ratio and also emphasized how the presence of some small depositional areas associated with the occurrence of high‐magnitude events influences the change in sediment connectivity at the catchment scale.

Repeatability of some phenotypic stability parameters - a resampling approach

Abstract The aim of this study was to estimate the repeatability of some phenotypic stability parameters: reliability index (I), regression coefficient (b 0 e b 1 ), ecovalence, and the mean of the absolute scores (MAS),and to check the implications of estimating stability parameters using a reduced number of environments. A data set comprising 25 common bean lines assessed in 36 environments was used. These environments comprise different locations, sowing seasons, and years. Different experimental groups, from three to 18 environments, were simulated. For each group, analysis of variance was performed and the stability parameters were estimated. The greater repeatability estimates were observed for the mean (b 0 ), followed by the I and b 1 . Ecovalence and MAS had the lowest values for the repeatability estimates, which is not desirable for breeding programs aiming at selection for high stability. The phenotypic stability parameter should not be estimated from fewer than 12 environments.

Robust Regression on Stationary Time Series: A Self‐Normalized Resampling Approach

This article extends the self‐normalized subsampling method of Bai et al. (2016) to the M‐estimation of linear regression models, where the covariate and the noise are stationary time series which may have long‐range dependence or heavy tails. The method yields an asymptotic confidence region for the unknown coefficients of the linear regression. The determination of these regions does not involve unknown parameters such as the intensity of the dependence or the heaviness of the distributional tail of the time series. Additional simulations can be found in a supplement. The computer codes are available from the authors.

A comparison of resampling methods for remote sensing classification and accuracy assessment

Maps that categorise the landscape into discrete units are a cornerstone of many scientific, management and conservation activities. The accuracy of these maps is often the primary piece of information used to make decisions about the mapping process or judge the quality of the final map. Variance is critical information when considering map accuracy, yet commonly reported accuracy metrics often do not provide that information. Various resampling frameworks have been proposed and shown to reconcile this issue, but have had limited uptake. In this paper, we compare the traditional approach of a single split of data into a training set (for classification) and test set (for accuracy assessment), to a resampling framework where the classification and accuracy assessment are repeated many times. Using a relatively simple vegetation mapping example and two common classifiers (maximum likelihood and random forest), we compare variance in mapped area estimates and accuracy assessment metrics (overall accuracy, kappa, user, producer, entropy, purity, quantity/allocation disagreement). Input field data points were repeatedly split into training and test sets via bootstrapping, Monte Carlo cross-validation (67:33 and 80:20 split ratios) and k-fold (5-fold) cross-validation. Additionally, within the cross-validation, we tested four designs: simple random, block hold-out, stratification by class, and stratification by both class and space. A classification was performed for every split of every methodological combination (100’s iterations each), creating sampling distributions for the mapped area of each class and the accuracy metrics. We found that regardless of resampling design, a single split of data into training and test sets results in a large variance in estimates of accuracy and mapped area. In the worst case, overall accuracy varied between ~40–80% in one resampling design, due only to random variation in partitioning into training and test sets. On the other hand, we found that all resampling procedures provided accurate estimates of error, and that they can also provide confidence intervals that are informative about the performance and uncertainty of the classifier. Importantly, we show that these confidence intervals commonly encompassed the magnitudes of increase or decrease in accuracy that are often cited in literature as justification for methodological or sampling design choices. We also show how a resampling approach enables generation of spatially continuous maps of classification uncertainty. Based on our results, we make recommendations about which resampling design to use and how it could be implemented. We also provide a fully worked mapping example, which includes traditional inference of uncertainty from the error matrix and provides examples for presenting the final map and its accuracy.

Creation of a tissue expression biomarker-augmented prognostic model for patients with metastatic renal cell carcinoma.

703 Background: Clinical and pathologic factors alone have limited prognostic ability in patients with metastatic clear cell renal cell carcinoma (ccRCC). We sought to determine if tissue biomarkers, along with our previously reported clinical metastases score, can be used to predict cancer specific survival (CSS) in patients with metastatic ccRCC. Methods: Patients with metastatic ccRCC who underwent nephrectomy between 1990-2004 were identified using the Mayo Clinic Nephrectomy Registry. Sections from paraffin-embedded primary tumor tissue blocks were used for immunohistochemistry staining for PD-1, B7-H1 (PD-L1), B7-H3, Bim (downstream pro-apoptotic signaling molecule in PD-1 pathway), CA-IX, IMP3, Ki67, and survivin. CSS was the primary outcome. Biomarkers that were significantly associated with CSS after adjusting for the metastases score were used to develop a biomarker-specific multivariable model using a bootstrap resampling approach and forward selection. Predictive ability was summarized using a bootstrap-corrected c-index. Results: The cohort included 602 patients, 192 (32%) with metastases at diagnosis and 410 (68%) who developed metastases after nephrectomy. Median follow-up among survivors was 9.6 years (IQR 4.2,12.8) and 504 patients died of RCC. Bim, IMP3, Ki67, and survivin expression were significantly associated with CSS after adjusting for the metastases score and were used to develop a biomarker-specific model. High Bim (HR 1.44; 95%CI 1.16-1.78; p < 0.001), high survivin (HR 1.35; 95%CI 1.08-1.68; p = 0.008), and the metastases score (HR 1.13 per one point; 95%CI 1.10-1.16; p < 0.001) were retained as independent predictors in the final multivariable model (c-index 0.69). Conclusions: We created a prognostic model combining the clinical metastases score and two primary tissue expression biomarkers, Bim and survivin, for patients with metastatic RCC who underwent nephrectomy. External validation will be required prior to clinical use.

Three resampling approaches based on method of fragments for daily‐to‐subdaily precipitation disaggregation

ABSTRACTPrecipitation data of finer timescale and higher spatial density are crucial for continuous hydrological modelling and flood risk assessment. Disaggregation methods are often used to transform the coarser‐timescale precipitation data into finer resolutions. The nonparametric approach based on method of fragments (MOF) has received broad attention in precipitation disaggregation literature, given its superiority in reproducing the at‐site statistical attributes. However, a detailed literature review has shown that the MOF‐based resampling approaches are mainly focused in the single‐site precipitation disaggregation context, which may subject to limitations such as the unavailability of at‐site precipitation records and the incapability to preserve the inter‐site correlation structure. To address these issues, we propose three resampling approaches based on MOF. The first approach is a single‐site interval‐based resampling approach which only draws subdaily fragment vectors from at‐site record. The second one extends the first one to a regionalized version where subdaily fragment vectors are drawn from both the at‐site and neighbouring stations. The third one is a multi‐site approach developed to preserve the observed inter‐site correlation. The performances of the three methods are evaluated with applications to daily‐to‐hourly precipitation disaggregation at six rain gauges in Singapore and eight precipitation stations in Wangkuai reservoir catchment in northern China. An elaborate list of performance measures, including standard validation statistics, spatial correlation, inter‐day connectivity, annual extreme analysis, and intra‐day dry and wet spell characteristics are used to assess the performance. The proposed three methods are shown to be effective in reproducing the at‐site attributes, and no significant deterioration of performance is observed when moving from the single‐site method to the regionalized and multi‐site versions. As expected, the multi‐site approach is the only one method that is able to reconstruct the spatial correlation in the disaggregated precipitation field. The approaches can be applied for daily‐to‐subdaily precipitation disaggregation in different regions.

Surface Fitting for Quasi Scattered Data from Coordinate Measuring Systems.

Non-uniform rational B-spline (NURBS) surface fitting from data points is wildly used in the fields of computer aided design (CAD), medical imaging, cultural relic representation and object-shape detection. Usually, the measured data acquired from coordinate measuring systems is neither gridded nor completely scattered. The distribution of this kind of data is scattered in physical space, but the data points are stored in a way consistent with the order of measurement, so it is named quasi scattered data in this paper. Therefore they can be organized into rows easily but the number of points in each row is random. In order to overcome the difficulty of surface fitting from this kind of data, a new method based on resampling is proposed. It consists of three major steps: (1) NURBS curve fitting for each row, (2) resampling on the fitted curve and (3) surface fitting from the resampled data. Iterative projection optimization scheme is applied in the first and third step to yield advisable parameterization and reduce the time cost of projection. A resampling approach based on parameters, local peaks and contour curvature is proposed to overcome the problems of nodes redundancy and high time consumption in the fitting of this kind of scattered data. Numerical experiments are conducted with both simulation and practical data, and the results show that the proposed method is fast, effective and robust. What’s more, by analyzing the fitting results acquired form data with different degrees of scatterness it can be demonstrated that the error introduced by resampling is negligible and therefore it is feasible.

A novel approach for epipolar resampling of cross-track linear pushbroom imagery using orbital parameters model

This paper presents a novel approach to epipolar resampling of cross-track linear pushbroom imagery using orbital parameters model (OPM). The backbone of the proposed method relies on modification of attitude parameters of linear array stereo imagery in such a way to parallelize the approximate conjugate epipolar lines (ACELs) with the instantaneous base line (IBL) of the conjugate image points (CIPs). Afterward, a complementary rotation is applied in order to parallelize all the ACELs throughout the stereo imagery. The new estimated attitude parameters are evaluated based on the direction of the IBL and the ACELs. Due to the spatial and temporal variability of the IBL (respectively changes in column and row numbers of the CIPs) and nonparallel nature of the epipolar lines in the stereo linear images, some polynomials in the both column and row numbers of the CIPs are used to model new attitude parameters. As the instantaneous position of sensors remains fix, the digital elevation model (DEM) of the area of interest is not required in the resampling process. According to the experimental results obtained from two pairs of SPOT and RapidEye stereo imagery with a high elevation relief, the average absolute values of remained vertical parallaxes of CIPs in the normalized images were obtained 0.19 and 0.28 pixels respectively, which confirm the high accuracy and applicability of the proposed method.

Applying causal mediation analysis to personality disorder research.

This article is designed to address fundamental issues in the application of causal mediation analysis to research on personality disorders. Causal mediation analysis is used to identify mechanisms of effect by testing variables as putative links between the independent and dependent variables. As such, it would appear to have relevance to personality disorder research. It is argued that proper implementation of causal mediation analysis requires that investigators take several factors into account. These factors are discussed under 5 headings: variable selection, model specification, significance evaluation, effect size estimation, and sensitivity testing. First, care must be taken when selecting the independent, dependent, mediator, and control variables for a mediation analysis. Some variables make better mediators than others and all variables should be based on reasonably reliable indicators. Second, the mediation model needs to be properly specified. This requires that the data for the analysis be prospectively or historically ordered and possess proper causal direction. Third, it is imperative that the significance of the identified pathways be established, preferably with a nonparametric bootstrap resampling approach. Fourth, effect size estimates should be computed or competing pathways compared. Finally, investigators employing the mediation method are advised to perform a sensitivity analysis. Additional topics covered in this article include parallel and serial multiple mediation designs, moderation, and the relationship between mediation and moderation. (PsycINFO Database Record

A Nomogram to Predict Prognosis in Malignant Pleural Mesothelioma.

Malignant pleural mesothelioma (MPM) is a rare malignancy with heterogeneous outcomes. This study was aimed to develop a nomogram to precisely and visually predict survival of MPM patients. Data from Surveillance, Epidemiology, and End Results database (1973-2014) on MPM were screened and retrieved. The prognostic effects of variables, including age, sex, race, year of diagnosis, laterality, histology, tumor stage, surgery, chemotherapy, and radiotherapy were analyzed using Kaplan-Meier method and Cox proportional hazard model. A nomogram was formulated to predict overall survival of MPM patients. A total of 1092 cases who met inclusion criteria were included in this study. The overall 1-, 2-, and 3-year survival rate in the entire cohort was 45.1, 23.0, and 12.1%, with median survival of 11months. Cox regression analysis showed that age (P<0.001), race (P=0.003), histology type (P<0.001), T stage (P<0.001), M stage (P<0.001), TNM stage (P<0.001), cancer-directed surgery (P<0.001), and chemotherapy (P<0.001) were all independent prognostic factors of MPM patients. A nomogram was established based on the results of multivariate analysis. The internal bootstrap resampling approach suggested the nomogram had sufficient discriminatory power with the C-index of 0.705 (95% CI 0.681-0.729). The calibration plots also demonstrated good consistence between the prediction and the observation. We developed a nomogram to accurately predict clinical outcomes of MPM patients based on individual characteristics. Risk stratification by the survival nomogram could optimize individual therapies and follow-up.

Cluster analysis on a sphere: Application to magnetizations from metasediments of the Jack Hills, Western Australia

Metasediments of the Jack Hills contain the oldest known terrestrial minerals in the form of zircons nearly 4.4 billion years old. Paleointensity data from these zircons provide evidence for a Hadean geodynamo as old as 4.2 billion years old. Given the importance of these zircons for constraining the earliest history of the core, it is vital to understand the fidelity of the zircon record. A fundamental aspect providing context for the preservation of primary magnetic signals is the nature of overprints predicted to have been imparted on rocks of the Jack Hills due to Archean to Proterozoic metamorphic events. To be viable magnetic records of a Hadean geodynamo, zircon magnetization directions should differ from these secondary magnetizations. To evaluate these secondary magnetizations, we report paleomagnetic analyses of a comprehensive sampling of 68 quartzite cobble-sized clasts from the Jack Hills metasediments ∼0.5 to 1.0 km from the Discovery Site (which has yielded the oldest zircons and paleofield estimates). While application of standard paleomagnetic tests suggests that the ensemble of cobble directions cannot be distinguished from those drawn from a random distribution, a new cluster analysis of directions on a sphere and non-parametric resampling approaches reveal significant directions amongst subsets of the data. One, isolated at the lowest temperature analyzed [200 to 300 °C, Declination (Dec.) = 316.8°, Inclination (Inc.) =−51.1°] appears to be dominated by the present day field. Another, isolated at higher (but still relatively low unblocking temperatures that we call “intermediate”, of ∼350–500 °C, Dec. = 243.8°, Inc. = 9.5°) agrees with a magnetic overprint isolated from the secondary Cr–Fe mica fuchsite isolated from the Jack Hills Discovery site, passing a field test at the 80% confidence level. No evidence is found in our data, or in the data of others collected on similar Jack Hills lithologies, for a widespread 1 Ga remagnetization event. Instead, we interpret the most prevalent secondary magnetization of the quartzite (i.e., intermediate unblocking) and the fuchsite characteristic remanent magnetization to be ∼2.65 Ga in age, coincident with peak metamorphism (as high as ca. 475 °C) of the Jack Hills. The presence of this distinct secondary magnetization, its difference from that recorded by Jack Hills zircons at high unblocking temperatures, and the lack of a dominant remagnetization direction at high unblocking temperatures in the cobble data (the expected result for a primary magnetization), lends further support to the fidelity of the Hadean geomagnetic record. The presence of the secondary magnetization also lends support to the conclusion that most of the Jack Hills metasediments were deposited in the Archean, with only minor reworking and potential tectonic interleaving of Proterozoic components. Overall, the application of the new directional cluster analysis presented here has the potential to reveal magnetic directions in highly scattered data sets, typical of weakly magnetized coarse-grained sedimentary rocks

Economic productivity loss due to breast cancer in Norway – a case control study using the human capital approach

We estimate the productivity loss attributable to breast cancer in Norway, using a human capital approach where the value to the national economy of subjects’ labor is estimated through their income. Since the approach takes the viewpoint of the society, it includes reduced labor due to mortality as well as reduction among survivors. A case-control design was used. The Norwegian Cancer Registry identified 2,010 patients aged 45-54 years, with breast cancer as their first life-long malignancy diagnosis in 1992-1996. Statistics Norway matched these with random controls of the same age, marital status and municipality residency, and provided data on pension qualifying income until the year 2005, inflation adjusted to 2012 €. The effect of the cancer diagnosis on income throughout the follow-up period was estimated as the difference between the cases’ and controls’ income, corrected for systematic differences between cases and controls before the diagnosis. A quadratic curve approximation was used to estimate effects after the 13 years follow-up period, and a bootstrap resampling approach was applied to compute confidence intervals (CI). Regression modeling was used to estimate life-long productivity loss as a function of age at diagnosis. This was combined with the current Norwegian age distribution of breast cancer incidence, to estimate the national productivity loss due to breast cancer. For our cohort, the 13-years productivity loss attributable to the diagnosis was estimated to 102,600 € per case, with 95% CI (88,500, 116,700). The life-long estimate was 119,200 €, CI (95,400, 155,600). The annual national productivity loss was estimated to 179,900,000 €, or 58,200 € per case. For those aged &lt; 65 at diagnosis, the estimate was 94,300 € per case. The estimated life-long productivity loss depends heavily on the age at diagnosis. The results can be used for evaluating the societal economic benefit from breast cancer prevention programs. Published: Online December 2017. In print December 2017.

Multi-scale and multi-site resampling of a study area in spatial genetics: implications for flying insect species.

The use of multiple sampling areas in landscape genetic analysis has been recognized as a useful way of generalizing the patterns of environmental effects on organism gene flow. It reduces the variability in inference which can be substantially affected by the scale of the study area and its geographic location. However, empirical landscape genetic studies rarely consider multiple sampling areas due to the sampling effort required. In this study, we explored the effects of environmental features on the gene flow of a flying long-horned beetle (Monochamus galloprovincialis) using a landscape genetics approach. To account for the unknown scale of gene flow and the multiple local confounding effects of evolutionary history and landscape changes on inference, we developed a way of resampling study areas on multiple scales and in multiple locations (sliding windows) in a single large-scale sampling design. Landscape analyses were conducted in 3*104 study areas ranging in scale from 220 to 1,000 km and spread over 132 locations on the Iberian Peninsula. The resampling approach made it possible to identify the features affecting the gene flow of this species but also showed high variability in inference among the scales and the locations tested, independent of the variation in environmental features. This method provides an opportunity to explore the effects of environmental features on organism gene flow on the whole and reach conclusions about general landscape effects on their dispersal, while limiting the sampling effort to a reasonable level.

Comparison of variable selection methods for high-dimensional survival data with competing events

BackgroundIn the era of personalized medicine, it's primordial to identify gene signatures for each event type in the context of competing risks in order to improve risk stratification and treatment strategy. Until recently, little attention was paid to the performance of high-dimensional selection in deriving molecular signatures in this context. In this paper, we investigate the performance of two selection methods developed in the framework of high-dimensional data and competing risks: Random survival forest and a boosting approach for fitting proportional subdistribution hazards models. MethodsUsing data from bladder cancer patients (GSE5479) and simulated datasets, stability and prognosis performance of the two methods were evaluated using a resampling strategy. For each sample, the data set was split into 100 training and validation sets. Molecular signatures were developed in the training sets by the two selection methods and then applied on the corresponding validation sets. ResultsRandom survival forest and boosting approach have comparable performance for the prediction of survival data, with few selected genes in common. Nevertheless, many different sets of genes are identified by the resampling approach, with a very small frequency of genes occurrence among the signatures. Also, the smaller the training sample size, the lower is the stability of the signatures. ConclusionRandom survival forest and boosting approach give good predictive performance but gene signatures are very unstable. Further works are needed to propose adequate strategies for the analysis of high-dimensional data in the context of competing risks.

Improving the quality of extracting dynamics from interspike intervals via a resampling approach

We address the problem of improving the quality of characterizing chaotic dynamics based on point processes produced by different types of neuron models. Despite the presence of embedding theorems for non-uniformly sampled dynamical systems, the case of short data analysis requires additional attention because the selection of algorithmic parameters may have an essential influence on estimated measures. We consider how the preliminary processing of interspike intervals (ISIs) can increase the precision of computing the largest Lyapunov exponent (LE). We report general features of characterizing chaotic dynamics from point processes and show that independently of the selected mechanism for spike generation, the performed preprocessing reduces computation errors when dealing with a limited amount of data.

Estimation of malaria parasite reservoir coverage using reactive case detection and active community fever screening from census data with rapid diagnostic tests in southern Zambia: a re-sampling approach

Background and methods In areas where malaria transmission has been suppressed by vector control interventions many malaria control and elimination programmes are actively seeking new interventions to further reduce malaria prevalence, incidence and transmission. Malaria infection prevalence and incidence has been shown to cluster geographically, especially at lower transmission levels, and as such a reactive strategy is frequently used, by which index cases presenting to a passive surveillance system are used to target small areas for testing and treatment, reactive case detection (RCD), or focal drug administration (fDA). This study utilizes geo-located data from a census with parasitological testing with rapid diagnostic tests (RDTs) and treatment-seeking data collection conducted in southern Zambia to estimate the coverage of RCD or fDA in terms of the population and parasite reservoir as well as the operational requirements of such strategies, using a re-sampling algorithm developed exclusively for this purpose. This re-sampling algorithm allows for the specification of several parameters, such that different operational variants of these reactive strategies can be examined, including varying the search radius, screening for fever, or presumptive treatment (fDA).ResultsResults indicate that RCD, fDA and active fever screening followed by RCD, even with search radii over several hundered meters will only yield limited coverage of the RDT positive parasite reservoir during a short period. Long-term use of these strategies may increase this proportion. Reactive strategies detect a higher proportion of the reservoir of infections than random searches, but this effect appears to be greater in areas of low, but not moderate malaria prevalence in southern Zambia.DiscussionIncreases in the sensitivity of RDTs could also affect these results. The number of individuals and households that need to be searched increase rapidly, but approximately linearly with search radius.ConclusionsReactive strategies in southern Zambia yield improved identification of the parasite reservoir when targeted to areas with prevalence less than 10%. The operational requirements of delivering reactive strategies routinely are likely to prevent their uptake until prevalence falls far below this level.

The PIT-trap-A "model-free" bootstrap procedure for inference about regression models with discrete, multivariate responses.

Bootstrap methods are widely used in statistics, and bootstrapping of residuals can be especially useful in the regression context. However, difficulties are encountered extending residual resampling to regression settings where residuals are not identically distributed (thus not amenable to bootstrapping)—common examples including logistic or Poisson regression and generalizations to handle clustered or multivariate data, such as generalised estimating equations. We propose a bootstrap method based on probability integral transform (PIT-) residuals, which we call the PIT-trap, which assumes data come from some marginal distribution F of known parametric form. This method can be understood as a type of “model-free bootstrap”, adapted to the problem of discrete and highly multivariate data. PIT-residuals have the key property that they are (asymptotically) pivotal. The PIT-trap thus inherits the key property, not afforded by any other residual resampling approach, that the marginal distribution of data can be preserved under PIT-trapping. This in turn enables the derivation of some standard bootstrap properties, including second-order correctness of pivotal PIT-trap test statistics. In multivariate data, bootstrapping rows of PIT-residuals affords the property that it preserves correlation in data without the need for it to be modelled, a key point of difference as compared to a parametric bootstrap. The proposed method is illustrated on an example involving multivariate abundance data in ecology, and demonstrated via simulation to have improved properties as compared to competing resampling methods.