Non-parametric Resampling Technique Research Articles

Conditional Inference in Small Sample Scenarios Using a Resampling Approach

This paper discusses a non-parametric resampling technique in the context of multidimensional or multiparameter hypothesis testing of assumptions of the Rasch model. It is based on conditional distributions and it is suggested in small sample size scenarios as an alternative to the application of asymptotic or large sample theory. The exact sampling distribution of various well-known chi-square test statistics like Wald, likelihood ratio, score, and gradient tests as well as others can be arbitrarily well approximated in this way. A procedure to compute the power function of the tests is also presented. A number of examples of scenarios are discussed in which the power function of the test does not converge to 1 with an increasing deviation of the true values of the parameters of interest from the values specified in the hypothesis to be tested. Finally, an attempt to modify the critical region of the tests is made aiming at improving the power and an R package is provided.

Build a better bootstrap and the RAWR shall beat a random path to your door: phylogenetic support estimation revisited.

MotivationThe standard bootstrap method is used throughout science and engineering to perform general-purpose non-parametric resampling and re-estimation. Among the most widely cited and widely used such applications is the phylogenetic bootstrap method, which Felsenstein proposed in 1985 as a means to place statistical confidence intervals on an estimated phylogeny (or estimate ‘phylogenetic support’). A key simplifying assumption of the bootstrap method is that input data are independent and identically distributed (i.i.d.). However, the i.i.d. assumption is an over-simplification for biomolecular sequence analysis, as Felsenstein noted.ResultsIn this study, we introduce a new sequence-aware non-parametric resampling technique, which we refer to as RAWR (‘RAndom Walk Resampling’). RAWR consists of random walks that synthesize and extend the standard bootstrap method and the ‘mirrored inputs’ idea of Landan and Graur. We apply RAWR to the task of phylogenetic support estimation. RAWR’s performance is compared to the state-of-the-art using synthetic and empirical data that span a range of dataset sizes and evolutionary divergence. We show that RAWR support estimates offer comparable or typically superior type I and type II error compared to phylogenetic bootstrap support. We also conduct a re-analysis of large-scale genomic sequence data from a recent study of Darwin’s finches. Our findings clarify phylogenetic uncertainty in a charismatic clade that serves as an important model for complex adaptive evolution.Availability and implementationData and software are publicly available under open-source software and open data licenses at: https://gitlab.msu.edu/liulab/RAWR-study-datasets-and-scripts.

Forecasting storage capacity using exponential smoothing method

Purpose: Analysis of the production and storage capacity of soybeans and corn in Mato Grosso (Brazil) and its projections until the year 2022. Methodology/Approach: Data regarding Mato Grosso’s soy and corn production, area planted, and storage and yield capacity were organized for the years 1995 to 2018. Exponential smoothing models were then used to preview the grain storage balance for the period between 2019 and 2022. Findings: Grain production is expected to grow 11.9%, while the increase in storage capacity is expected to be 9.7%. Therefore, the results showed that the gap between production and storage may increase by 118%, between the harvests of 2017/18 and 2021/22. Moreover, the deficit in storage capacity will represent 53% of the total harvest in 2021/22. Research Limitation/implication: Interferences in the analyzed variables, such as economic, environmental, or technological events, may affect the forecasts’ outcomes. Originality/Value of paper: The estimation combines exponential smoothing models with a non-parametric resampling technique.

An Alternative for Estimating the Design Flood Interval of Agricultural Reservoirs under Climate Change Using a Non-Parametric Resampling Technique

Agricultural reservoirs play such a central role in supplying water to rural areas that it is essential to properly estimate the design flood for agricultural reservoirs under climate change. The objective of this study was to estimate the inflow design flood interval using a non-parametric resampling technique for agricultural reservoirs under climate change. This study suggested an alternative method to point estimation using insufficient past data by providing the interval of the inflow design flood under the representative concentration pathway. To estimate the interval of the inflow design flood, we employed the bootstrap technique, which estimated the confidence interval corresponding to the 95% confidence level. This study covered a spatial range of 30 agricultural reservoirs in South Korea and a temporal range of past and three future representative periods: the base period (2015s: 1986–2015) and future periods (2040s: 2011–2040, 2070s: 2041–2070, 2100s: 2071–2100). We analyzed the results of a 200-year return period and 24-hour duration as a representative case. For the 97.5th bias-corrected and accelerated percentile value, the overall inflow design floods were larger than the base period value (2015s) with the safety factor applied. The northern and midwestern regions of South Korea showed relatively greater changes than the southeastern region. Some agricultural reservoirs showed a decrease in the design flood during the 2040s but generally increased after the 2070s. Through the non-parametric resampling technique, the interval estimation was provided considering the uncertainty of the inflow design flood. By presenting the results for three periods, we can provide policymakers with information to select according to the target period. The findings may provide an essential step in replacing a safety factor used for determining the design flood of agricultural reservoirs with the confidence interval calculated in accordance with statistical characteristics.

VGC ANALYZER: A SOFTWARE FOR STATISTICAL ANALYSIS OF FULLY CROSSED MULTIPLE-READER MULTIPLE-CASE VISUAL GRADING CHARACTERISTICS STUDIES.

Visual grading characteristics (VGC) analysis is a non-parametric rank-invariant method for analysis of visual grading data. In VGC analysis, image quality ratings for two different conditions are compared by producing a VGC curve, similar to how the ratings for normal and abnormal cases in receiver operating characteristic (ROC) analysis are used to create an ROC curve. The use of established ROC software for the analysis of VGC data has therefore previously been proposed. However, the ROC analysis is based on the assumption of independence between normal and abnormal cases. In VGC analysis, this independence cannot always be assumed, e.g. if the ratings are based on the same patients imaged under both conditions. A dedicated software intended for analysis of VGC studies, which takes possible dependencies between ratings into account in the statistical analysis of a VGC study, has therefore been developed. The software-VGC Analyzer-determines the area under the VGC curve and its uncertainty using non-parametric resampling techniques. This article gives an introduction to VGC Analyzer, describes the types of analyses that can be performed and instructs the user about the input and output data.

Risk Analysis Using Earned Value

Control and feedback in complex and sensitive systems with different resources and properties in uncertainty conditions are one of the significant challenges for project managers. Therefore, in this paper an integrated model with quantitative and qualitative approach based on Earned value management and risk analysis methods proposed to decrease deviations in time and cost estimation at complete (EAC(t) & EAC). Using the proposed performance indexes based on risk scores obtained by non-parametric resampling technique, namely bootstrap, calculating more accurate estimations of final cost and duration at lower and upper band with various confidence levels will be possible. Finally, an illustrative case study presented demonstrating the proposed model can be efficiently applied through real case software projects.

Project time and cost estimate at completion based on non-parametric resampling with interval risk

Control and feedback in complex and sensitive systems with different resources and properties in uncertainty conditions are one of the significant challenges for project managers. Therefore, in this paper, an integrated model with quantitative and qualitative approach based on earned value management and risk analysis methods proposed to decrease deviations in time and cost estimation at complete (EAC(t) and EAC). Using the proposed performance indexes based on risk scores obtained by non-parametric resampling technique, namely bootstrap, calculating more accurate estimations of final cost and duration at lower and upper band with various confidence levels will be possible. Finally, an illustrative case study presented demonstrating the proposed model can be efficiently applied through real case software projects.

Statistical nonlinear analysis for reliable promotion decision-making

New economic conditions have led to innovations in retail industries, such as more dynamic retail approaches based on flexible strategies. We propose and compare different approaches incorporating nonlinear methods for promotional decision-making using retail aggregated data registered at the point of the sale. Specifically, this paper describes a reliable quantification tool as an effective information system leveraged on recent and historical data that provides managers with an operative vision. Furthermore, a new set of indicators are proposed to evaluate the reliability and stability of the data model in the multidimensional feature space by using nonparametric resampling techniques. This allows the user to make a clearer comparison among linear, nonlinear, static, and dynamic data models, and to identify the uncertainty of different feature space regions, for example, corresponding to the most frequent deal features. This methodology allows retailers to use aggregated data in suitable conditions that will result in acceptable confidence intervals. To test the proposed methodology, we used a database containing the sales history of representative products registered by a Spanish retail chain. The results indicate that: (1) the deal effect curve analysis and the time series linear model do not provide enough expressive capacity, and (2) nonlinear promotional models more accurately follow the actual sales pattern obtained in response to the implemented sales promotions. The quarterly temporal analysis conducted enabled the authors to identify long-term changes in the dynamics of the model for several products, especially during the early stage of most recent economic crisis, consistent with the information provided by the reliability indices in terms of the feature space. We conclude that the proposed method provides a reliable operative tool for decision support, allowing retailers to alter their strategies to accommodate consumer behavior.

An integrated approach for risk assessment in port projects

Port risk management is utilized to describe a sequence of the analysis and management activities focused on creating specific responses to inherent risks in port projects. Risk assessment is taken into account in the projects; however, it is still comparatively neglected for the assessment process with non-parametric resampling techniques and interval analysis. The required data and experts' judgments in developing countries are often small. Hence, traditional approaches cannot assist such problems remarkably. An integrated approach is proposed in this paper for risk assessment in port projects for the use of the bootstrap and interval computations. In an application example, the proposed approach is investigated in detail. Finally, the traditional and proposed approaches are compared.

An ExPosition of multivariate analysis with the singular value decomposition in R

ExPosition is a new comprehensive R package providing crisp graphics and implementing multivariate analysis methods based on the singular value decomposition (svd). The core techniques implemented in ExPosition are: principal components analysis, (metric) multidimensional scaling, correspondence analysis, and several of their recent extensions such as barycentric discriminant analyses (e.g., discriminant correspondence analysis), multi-table analyses (e.g.,multiple factor analysis, Statis, and distatis), and non-parametric resampling techniques (e.g., permutation and bootstrap). Several examples highlight the major differences between ExPosition and similar packages. Finally, the future directions of ExPosition are discussed.

Government Insurance Program Design, Incentive Effects, and Technology Adoption: The Case of Skip‐Row Crop Insurance

Can the availability of poorly‐designed government insurance alter technology adoption decisions? A theoretical model of technology adoption and insurance incentive effects for a high‐ and low‐risk technology is developed and explored empirically using a unique dataset of skip‐row agronomic trial data. A multivariate nonparametric resampling technique is developed, which augments the trial data with a larger dataset of conventional yields to improve estimation efficiency. Skip‐row adoption is found to increase mean yields and reduce risk in areas prone to drought. RMA insurance rules have incentive‐distorting impacts which disincentivize skip‐row adoption.

A novel approach based on non-parametric resampling with interval analysis for large engineering project risks

The issue of risk assessment has been always the matter of debate in large engineering projects (LEPs). The assessment is an indispensable means for the projects to accomplish their objectives. It is firmly accepted that LEPs are particularly subject to more potential risks than other business activities because of their complexity, uncertainty and ambiguity. These characteristics are often conducive to small sample sizes of the gathered risk data in practice. Consequently, traditional statistical techniques cannot contribute significantly to analyze the risk data. The non-parametric resampling technique, namely bootstrap, has been used subsequently to solve numerous complicated problems and evaluate the accuracy of a parameter estimator in situations where commonly used techniques are not valid. It is also more natural, applicable and simple to estimate the risk data in an interval form under decision-making process by considering the concept of safety by professional experts in LEPs. Hence, in this paper, a new approach based on bootstrap technique with the interval analysis is presented in the context of the project risk assessment. The proposed approach not only plays an important role in reducing risk data and saving time but also is more economical. A real case study is conducted to illustrate the applicability of the approach. Finally, the comparison results indicate that the proposed approach outperforms the traditional technique in terms of the accuracy and efficiency.

Bootstrap Technique for Risk Analysis with Interval Numbers in Bridge Construction Projects

Bridges are principal and vital transportation structures. If risk management is not considered in bridge construction projects, objectives cannot be delivered on time, on budget, or with suitable quality results. Risk data set sizes and experts’ judgments are not usually sufficient for analyzing significant risks in bridge construction projects; moreover, the statistical distributions for risk parameter estimates are usually unknown. Standard parametric statistical techniques cannot provide appropriate solutions for cases with small data sets or unknown distributions. This paper proposes a new hybrid approach by using a nonparametric resampling technique and interval computations for risk analysis, in particular, for bridge construction projects. Bootstrap techniques produce more accurate inferences for comparing parametric techniques and are an alternative when the underlying parametric assumptions are not considered. Increasingly, because of the complexity and uncertainty in decision making at bridge projects, it is easier or more natural to provide interval values for parts or all of decision-making judgments. Furthermore, the goal of reducing standard deviations for both risk probability and risk impact compared with the conventional approach is another conclusion of this paper. The proposed approach is applied to a case in Iran to show the validity of the approach.

Switching the pooling similarity distances: Mahalanobis for Euclidean

In recent years, catchment similarity measures based on flood seasonality have become popular alternatives for identifying hydrologically homogeneous pooling groups used in regional flood frequency analysis. Generally, flood seasonality pooling measures are less prone to errors and are more robust than measures based on flood magnitude data. However, they are also subject to estimation uncertainty resulting from sampling variability. Because of sampling variability, catchment similarity in flood seasonality can significantly deviate from the true similarity. Therefore sampling variability should be directly incorporated in the pooling algorithm to decrease the level of pooling uncertainty. This paper develops a new pooling approach that takes into consideration the sampling variability of flood seasonality measures used as pooling variables. A nonparametric resampling technique is used to estimate the sampling variability for the target site, as well as for every site that is a potential member of the pooling group for the target site. The variability is quantified by Mahalanobis distance ellipses. The similarity between the target site and the potential site is then assessed by finding the minimum confidence interval at which their Mahalanobis ellipses intersect. The confidence intervals can be related to regional homogeneity, which allows the target degree of regional homogeneity to be set in advance. The approach is applied to a large set of catchments from Great Britain, and its performance is compared with the performance of a previously used pooling technique based on the Euclidean distance. The results demonstrate that the proposed approach outperforms the previously used approach in terms of the overall homogeneity of delineated pooling groups in the study area.

On the Finite Sample Accuracy of Nonparametric Resampling Algorithms for Economic Time Series

In recent years, there has been increasing interest in nonparametric bootstrap inference for economic time series. Nonparametric resampling techniques help protect against overly optimistic inference in time series models of unknown structure. They are particularly useful for evaluating the fit of dynamic economic models in terms of their spectra, impulse responses, and related statistics, because they do not require a correctly specified economic model. Notwithstanding the potential advantages of nonparametric bootstrap methods, their reliability in small samples is questionable. In this paper, we provide a benchmark for the relative accuracy of several nonparametric resampling algorithms based on ARMA representations of four macroeconomic time series. For each algorithm, we evaluate the effective coverage accuracy of impulse response and spectral density bootstrap confidence intervals for standard sample sizes. We find that the autoregressive sieve approach based on the encompassing model is most accurate. However, care must be exercised in selecting the lag order of the autoregressive approximation.

On the Finite-Sample Accuracy of Nonparametric Resampling Algorithms for Economic Time Series

In recent years, there has been increasing interest in nonparametric bootstrap inference for economic time series. Nonparametric resampling techniques help protect against overly optimistic inference in time series models of unknown structure. They are particularly useful for evaluating the fit of dynamic economic models in terms of their spectra, impulse responses, and related statistics, because they do not require a correctly specified economic model. Notwithstanding the potential advantages of nonparametric bootstrap methods, their reliability in small samples is questionable. In this paper, we provide a benchmark for the relative accuracy of several nonparametric resampling algorithms based on ARMA representations of four macroeconomic time series. For each algorithm, we evaluate the effective coverage accuracy of impulse response and spectral density bootstrap confidence intervals for standard sample sizes. We find that the autoregressive sieve approach based on the encompassing model is most accurate. However, care must be exercised in selecting the lag order of the autoregressive approximation.

Multivariate nonparametric resampling scheme for generation of daily weather variables

A nonparametric resampling technique for generating daily weather variables at a site is presented. The method samples the original data with replacement while smoothing the empirical conditional distribution function. The technique can be thought of as a smoothed conditional Bootstrap and is equivalent to simulation from a kernel density estimate of the multivariate conditional probability density function. This improves on the classical Bootstrap technique by generating values that have not occurred exactly in the original sample and by alleviating the reproduction of fine spurious details in the data. Precipitation is generated from the nonparametric wet/dry spell model as described in Lall et al. [1995]. A vector of other variables (solar radiation, maximum temperature, minimum temperature, average dew point temperature, and average wind speed) is then simulated by conditioning on the vector of these variables on the preceding day and the precipitation amount on the day of interest. An application of the resampling scheme with 30 years of daily weather data at Salt Lake City, Utah, USA, is provided.