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

We present new sampling methods in finite population that allow to control the joint inclusion probabilities of units and especially the spreading of sampled units in the population. They are based on the use of renewal chains and multivariate discrete distributions to generate the difference of population ranks between two successive selected units. With a Bernoulli sampling design, these differences follow a geometric distribution, and with a simple random sampling design they follow a negative hypergeometric distribution. We propose to use other distributions and introduce a large class of sampling designs with and without fixed sample size. The choice of the rank-difference distribution allows us to control units joint inclusion probabilities with a relatively simple method and closed form formula. Joint inclusion probabilities of neighboring units can be chosen to be larger, or smaller, compared to those of Bernoulli or simple random sampling, thus allowing to more or less spread the sample on the population. This can be useful when neighboring units have similar characteristics or, on the contrary, are very different. A set of simulations illustrates the qualities of this method.

Sampling Designs on Finite Populations with Spreading Control Parameters

We present two seminal contributions of Corrado Gini on the theory of survey sampling and the theory of inequalities: the idea of balanced sampling and the Gini inequality index. These contributions have contributed to the development of a fertile field of research.

The Legacy of Corrado Gini in Survey Sampling and Inequality Theory

Balanced sampling is a very efficient sampling design when the variable of interest is correlated to the auxiliary variables on which the sample is balanced A procedure to select balanced samples in a stratified population has previously been proposed Unfortunately, this procedure becomes very slow as the number of strata increases and it even fails to select samples for some large numbers of strata A new algorithm to select balanced samples in a stratified population is proposed This new procedure is much faster than the existing one when the number of strata is large Furthermore, this new procedure makes it possible to select samples for some large numbers of strata, which was impossible with the existing method Balanced sampling can then be applied on a highly stratified population when only a few units are selected in each stratum Finally, this algorithm turns out to be valuable for many applications as, for instance, for the handling of nonresponse

Yves Tillé

Papers

Sampling Designs on Finite Populations with Spreading Control Parameters

The Legacy of Corrado Gini in Survey Sampling and Inequality Theory

Fast balanced sampling for highly stratified population

Different Methods for Stratified Sampling [R package StratifiedSampling version 0.1.0]

Linearization and variance estimation of the Bonferroni inequality index