It's been over a decade since the first edition of Measurement Error in Nonlinear Models splashed onto the scene, and research in the field has certainly not cooled in the interim. In fact, quite the opposite has occurred. As a result, Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition has been revamped and ex

Measurement Error in Nonlinear Models: A Modern Perspective, Second Edition

In his seminal book, Shewhart (1931) makes no demand on the distribution of the characteristic to be plotted on a control chart. How then can we explain the idea that normality is, if not required, at least highly desirable? I believe that it has come about through the many statistical studies of control-chart behavior. If one is to study how a control chart behaves, it is necessary to relate it to some distribution. The obvious choice is the normal distribution because of its ubiquity as a satisfactory model. This is bolstered by the existence of the Central Limit Theorem.

Sampling: Design and Analysis

Here are two books on a topic new to Technometrics: statistical and mathematical demography. The first author of Applied Mathematical Demography wrote the first two editions of this book alone. The second edition was published in 1985. Professor Keyfritz noted in the Preface (p. vii) that at age 90 he had no interest in doing another edition; however, the publisher encouraged him to find a coauthor. The result is an additional focus for the book in the world of biology that makes it much more relevant for the sciences. The book is now part of the publisher’s series on Statistics for Biology and Health. Much of it, of course, focuses on the many aspects of human populations. The new material focuses on mature population models, the particular focus of the new author (see, e.g., Caswell 2000). As one might expect from a book that was originally written in the 1970s, it does not include a lot of information on statistical computing. The new book by Alho and Spencer is focused on putting a better emphasis on statistics in the discipline of demography (Preface, p. vii). It is part of the publisher’s Series in Statistics. The authors are both statisticians, so the focus is on statistics as used for demographic problems. The authors are targeting human applications, so their perspective on science does not extend any further than epidemiology. The book actually strikes a good balance between statistical tools and demographic applications. The authors use the first two chapters to teach statisticians about the concepts of demography. The next four chapters are very similar to the statistics content found in introductory books on survival analysis, such as the recent book by Kleinbaum and Klein (2005), reported by Ziegel (2006). The next three chapters are focused on various aspects of forecasting demographic rates. The book concludes with chapters focusing on three areas of applications: errors in census numbers, financial applications, and small-area estimates.

Univariate Discrete Distributions

Customer churn is often a rare event in service industries, but of great interest and great value. Until recently, however, class imbalance has not received much attention in the context of data mining [Weiss, G. M. (2004). Mining with rarity: A unifying framework. SIGKDD Explorations, 6(1), 7-19]. In this study, we investigate how we can better handle class imbalance in churn prediction. Using more appropriate evaluation metrics (AUC, lift), we investigated the increase in performance of sampling (both random and advanced under-sampling) and two specific modelling techniques (gradient boosting and weighted random forests) compared to some standard modelling techniques. AUC and lift prove to be good evaluation metrics. AUC does not depend on a threshold, and is therefore a better overall evaluation metric compared to accuracy. Lift is very much related to accuracy, but has the advantage of being well used in marketing practice [Ling, C., & Li, C. (1998). Data mining for direct marketing problems and solutions. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD-98). New York, NY: AAAI Press]. Results show that under-sampling can lead to improved prediction accuracy, especially when evaluated with AUC. Unlike Ling and Li [Ling, C., & Li, C. (1998). Data mining for direct marketing problems and solutions. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD-98). New York, NY: AAAI Press], we find that there is no need to under-sample so that there are as many churners in your training set as non churners. Results show no increase in predictive performance when using the advanced sampling technique CUBE in this study. This is in line with findings of Japkowicz [Japkowicz, N. (2000). The class imbalance problem: significance and strategies. In Proceedings of the 2000 international conference on artificial intelligence (IC-AI'2000): Special track on inductive learning, Las Vegas, Nevada], who noted that using sophisticated sampling techniques did not give any clear advantage. Weighted random forests, as a cost-sensitive learner, performs significantly better compared to random forests, and is therefore advised. It should, however always be compared to logistic regression. Boosting is a very robust classifier, but never outperforms any other technique.

https://sci2s.ugr.es/keel/pdf/specific/articulo/burez_handling_2009.pdf

Handling class imbalance in customer churn prediction

Effective land-based solutions to climate change mitigation require actions that maximize soil carbon storage without generating surplus nitrogen. Land management for carbon sequestration is most often informed by bulk soil carbon inventories, without considering the form in which carbon is stored, its capacity, persistency and nitrogen demand. Here, we present coupling of European-wide databases with soil organic matter physical fractionation to determine continental-scale forest and grassland topsoil carbon and nitrogen stocks and their distribution between mineral-associated and particulate organic matter pools. Grasslands and arbuscular mycorrhizal forests store more soil carbon in mineral-associated organic carbon, which is more persistent but has a higher nitrogen demand and saturates. Ectomycorrhizal forests store more carbon in particulate organic matter, which is more vulnerable to disturbance but has a lower nitrogen demand and can potentially accumulate indefinitely. The share of carbon between mineral-associated and particulate organic matter and the ratio between carbon and nitrogen affect soil carbon stocks and mediate the effects of other variables on soil carbon stocks. Understanding the physical distribution of organic matter in pools of mineral-associated versus particulate organic matter can inform land management for nitrogen-efficient carbon sequestration, which should be driven by the inherent soil carbon capacity and nitrogen availability in ecosystems. Land management strategies for enhancing soil carbon sequestration need to be tailored to different soil types, depending on how much organic matter is stored in pools of mineral-associated and particulate organic matter, suggests an analysis of soil organic matter across Europe.

Soil carbon storage informed by particulate and mineral-associated organic matter

Index-based investment products are becoming increasingly popular among passive managers. So far, empirical studies have focused on complex heuristic-related optimization techniques. In this article, we adopt a different perspective and apply a survey sampling framework in the context of stock market tracking. We describe a novel and automatic method that enables us to construct a small portfolio to track the Total Market Capitalization (TMC). The constructed portfolio is randomly selected using a new method of balanced sampling. Empirical studies are performed on constituents of the S&P500. Our findings suggest that balanced sampling portfolios can efficiently track a market.

Selection of balanced portfolios to track the main properties of a large market

Computational aspects of order πps sampling schemes

A variant of the Cox algorithm for the imputation of non-response of qualitative data

In order to overcome the problem of item nonresponse, random imputations are often used because they tend to preserve the distribution of the imputed variable. Among the methods of random imputation, the random hot-deck has the interesting property that the imputed values are observed values. We present a new random method of hot-deck imputation which enables us to select the imputed values such that some balancing equations are satised and such that the donors are selected in neighborhoods of the recipients.

/pdf/balanced-k-nearest-neighbor-imputation-1b7lpq4105.pdf

Balanced k-Nearest Neighbor Imputation

Summary The aim of sampling coordination is to maximize or minimize the overlap between several samples drawn successively in a population that changes over time. Therefore, the selection of a new sample will depend on the samples previously drawn. In order to obtain a larger (or smaller) overlap of the samples than the one obtained by independent selection of samples, a dependence between the samples must be introduced. This dependence will emphasize (or limit) the number of common units in the selected samples. Several methods for coordinating stratified samples, such as the Kish & Scott method, the Cotton & Hesse method, and the Rivi?re method, have already been developed. Using simulations, we compare the optimality of these methods and their quality of coordination. We present six new methods based on permanent random numbers (PRNs) and microstrata. These new methods have the advantage of allowing us to choose between positive or negative coordination with each of the previous samples. Simulations are run to test the validity of each of them.

/pdf/sampling-procedures-for-coordinating-stratified-samples-env7l5uaaw.pdf

Yves Tillé

Papers

Selection of balanced portfolios to track the main properties of a large market

Computational aspects of order πps sampling schemes

A variant of the Cox algorithm for the imputation of non-response of qualitative data

Balanced k-Nearest Neighbor Imputation

Sampling Procedures for Coordinating Stratified Samples: Methods Based on Microstrata