Metrics and barycenters for point pattern data

We introduce the transport–transform and the relative transport–transform metrics between finite point patterns on a general space, which provide a unified framework for earlier point pattern metrics, in particular the generalized spike time and the normalized and unnormalized optimal subpattern assignment metrics. Our main focus is on barycenters, i.e., minimizers of a q-th-order Fréchet functional with respect to these metrics. We present a heuristic algorithm that terminates in a local minimum and is shown to be fast and reliable in a simulation study. The algorithm serves as a general plug-in method that can be applied to point patterns on any state space where an appropriate algorithm for solving the location problem for individual points is available. We present applications to geocoded data of crimes in Euclidean space and on a street network, illustrating that barycenters serve as informative summary statistics. Our work is a first step toward statistical inference in covariate-based models of repeated point pattern observations.     

Description of a subfamily of the discrete pearson system as generalized-binomial distributions

In this paper we prove that a subfamily of distributions of the discrete Pearson, system, containing the Pólya distribution without replacement and hence the hypergeometric distribution, can be described as generalized-binomial distributions, i.e., the distribution of the number of successes which occur in independent trials. It is also shown that the probability of success will necessarily be different in each trial, with the exception of deterministic ones. As a consequence, all the properties of the generalized-binomial distribution will apply to this subfamily. Thus, applications to hypothesis testing and confidence intervals in the Pólya distribution are considered.     

An evaluation of change-point estimators for a sequence of normal observations with unknown parameters

Performance of maximum likelihood estimators (MLE) of the change-point in normal series is evaluated considering three scenarios where process parameters are assumed to be unknown. Different shifts, sample sizes, and locations of a change-point were tested. A comparison is made with estimators based on cumulative sums and Bartlett's test. Performance analysis done with extensive simulations for normally distributed series showed that the MLEs perform better (or equal) in almost every scenario, with smaller bias and standard error. In addition, robustness of MLE to non-normality is also studied.     

Accounting for the value of volunteer contributions

There is a need for an accounting paradigm that properly illustrates the value that nonprofits generate. Much of that value comes from volunteer contributions, which are significant but for the most part are not included in financial accounting statements, even though our research indicates that they account for almost a third of the value added by these organizations. This article reports the results of two studies related to measuring volunteer value in the accounting of nonprofits and then draws some policy implications from the research. The first study, a survey of 156 nonprofits in Canada, found that although about one‐third of the sample kept records of volunteer hours, only 3 percent included a value for them in their accounting statements. The second study, of nonprofit accountants, found that they did not feel that financial accounting statements properly represented the contribution of their organizations. A series of policy recommendations are presented, including suggestions for revising the regulations of accounting bodies for imputing volunteer value and creating accounting statements that better represent the contribution of nonprofits.     

DISTRIBUTIONAL CHARACTERIZATIONS THROUGH SCALING RELATIONS

Investigated here are aspects of the relation between the laws of X and Y where X is represented as a randomly scaled version of Y. In the case that the scaling has a beta law, the law of Y is expressed in terms of the law of X. Common continuous distributions are characterized using this beta scaling law, and choosing the distribution function of Y as a weighted version of the distribution function of X, where the weight is a power function. It is shown, without any restriction on the law of the scaling, but using a one‐parameter family of weights which includes the power weights, that characterizations can be expressed in terms of known results for the power weights. Characterizations in the case where the distribution function of Y is a positive power of the distribution function of X are examined in two special cases. Finally, conditions are given for existence of inverses of the length‐bias and stationary‐excess operators.     

Evaluation of statistical protocols for quality control of ecosystem carbon dioxide fluxes

Summary.  The process of quality control of micrometeorological and carbon dioxide (CO₂) flux data can be subjective and may lack repeatability, which would undermine the results of many studies. Multivariate statistical methods and time series analysis were used together and independently to detect and replace outliers in CO₂ flux data derived from a Bowen ratio energy balance system. The results were compared with those produced by five experts who applied the current and potentially subjective protocol. All protocols were tested on the same set of three 5-day periods, when measurements were conducted in an abandoned agricultural field. The concordance of the protocols was evaluated by using the experts' opinion (mean ± 1.96 standard deviations) as a reference interval (the Bland–Altman method). Analysing the 15 days together, the statistical protocol that combined multivariate distance, multiple linear regression and time series analysis showed a concordance of 93% on a 20-min flux basis and 87% on a daily basis (only 2 days fell outside the reference interval), and the overall flux differed only by 1.7% (3.2 g CO₂ m⁻²). An automated version of this or a similar statistical protocol could be used as a standard way of filling gaps and processing data from Bowen ratio energy balance and other techniques (e.g. eddy covariance). This would enforce objectivity in comparisons of CO₂ flux data that are generated by different research groups and streamline the protocols for quality control.     

On choosing between two nonlinear models estimated robustly. Some Monte Carlo evidence

The article considers the problem of choosing between two (possibly) nonlinear models that have been fitted to the same data using M-estimation methods. An asymptotically normally distributed test statistic that takes into account the fact that the models are fitted robustly is given. The new procedure is compared with other test statistics using a Monte Carlo study. We found that the presence of a competitive model either in the null or the alternative hipothesis affects the distributional properties of the tests, and that in the case that the data contains outlying observations the new procedure had a significantly higher power than the rest of the tests.     

Percentiles of sums of heavy-tailed random variables: beyond the single-loss approximation

A perturbative approach is used to derive approximations of arbitrary order to estimate high percentiles of sums of positive independent random variables that exhibit heavy tails. Closed-form expressions for the successive approximations are obtained both when the number of terms in the sum is deterministic and when it is random. The zeroth order approximation is the percentile of the maximum term in the sum. Higher orders in the perturbative series involve the right-truncated moments of the individual random variables that appear in the sum. These censored moments are always finite. As a result, and in contrast to previous approximations proposed in the literature, the perturbative series has the same form regardless of whether these random variables have a finite mean or not. For high percentiles, and specially for heavier tails, the quality of the estimate improves as more terms are included in the series, up to a certain order. Beyond that order the convergence of the series deteriorates. Nevertheless, the approximations obtained by truncating the perturbative series at intermediate orders are remarkably accurate for a variety of distributions in a wide range of parameters.