Machine learning and design of experiments with an application to product innovation in the chemical industry

Industrial statistics plays a major role in the areas of both quality management and innovation. However, existing methodologies must be integrated with the latest tools from the field of Artificial Intelligence. To this end, a background on the joint application of Design of Experiments (DOE) and Machine Learning (ML) methodologies in industrial settings is presented here, along with a case study from the chemical industry. A DOE study is used to collect data, and two ML models are applied to predict responses which performance show an advantage over the traditional modeling approach. Emphasis is placed on causal investigation and quantification of prediction uncertainty, as these are crucial for an assessment of the goodness and robustness of the models developed. Within the scope of the case study, the models learned can be implemented in a semi-automatic system that can assist practitioners who are inexperienced in data analysis in the process of new product development.     

M-quantile models with application to poverty mapping

Over the last decade there has been growing demand for estimates of population characteristics at small area level. Unfortunately, cost constraints in the design of sample surveys lead to small sample sizes within these areas and as a result direct estimation, using only the survey data, is inappropriate since it yields estimates with unacceptable levels of precision. Small area models are designed to tackle the small sample size problem. The most popular class of models for small area estimation is random effects models that include random area effects to account for between area variations. However, such models also depend on strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow for outlier robust inference. An alternative approach to small area estimation that is based on the use of M-quantile models was recently proposed by Chambers and Tzavidis (Biometrika 93(2):255–268, 2006) and Tzavidis and Chambers (Robust prediction of small area means and distributions. Working paper, 2007). Unlike traditional random effects models, M-quantile models do not depend on strong distributional assumption and automatically provide outlier robust inference. In this paper we illustrate for the first time how M-quantile models can be practically employed for deriving small area estimates of poverty and inequality. The methodology we propose improves the traditional poverty mapping methods in the following ways: (a) it enables the estimation of the distribution function of the study variable within the small area of interest both under an M-quantile and a random effects model, (b) it provides analytical, instead of empirical, estimation of the mean squared error of the M-quantile small area mean estimates and (c) it employs a robust to outliers estimation method. The methodology is applied to data from the 2002 Living Standards Measurement Survey (LSMS) in Albania for estimating (a) district level estimates of the incidence of poverty in Albania, (b) district level inequality measures and (c) the distribution function of household per-capita consumption expenditure in each district. Small area estimates of poverty and inequality show that the poorest Albanian districts are in the mountainous regions (north and north east) with the wealthiest districts, which are also linked with high levels of inequality, in the coastal (south west) and southern part of country. We discuss the practical advantages of our methodology and note the consistency of our results with results from previous studies. We further demonstrate the usefulness of the M-quantile estimation framework through design-based simulations based on two realistic survey data sets containing small area information and show that the M-quantile approach may be preferable when the aim is to estimate the small area distribution function.     

Analysis of the spread of Rhynchophorus ferrugineus in an urban area,using GIS techniques: a study case in Central Italy

Urban Ecosystems - The spread of the red palm weevil is now a very severe problem on a global scale. In this paper, we investigate its spread during the 2007–2013 period in a coastal urban...     

Model-based clustering with sparse covariance matrices

Statistics and Computing - Finite Gaussian mixture models are widely used for model-based clustering of continuous data. Nevertheless, since the number of model parameters scales quadratically with...     

Gender Roles and Internalized Sexual Stigma in Gay and Lesbian Persons: A Quadratic Relation

Purpose: No previous research has deeply investigated the association between gay men and lesbians’ adherence to gender roles and their internalized sexual stigma. This study suggests a potential non-linear correlation between these factors. Methods: Seventy-five gay men and 70 lesbians responded to self-report questionnaires. Hierarchical multiple regression analyses were conducted to investigate linear, quadratic, and exponential models and to determine which model best explained the correlation. Results: The quadratic model best explained the correlation, showing no gender differences. Conclusion: Adherence to gender roles constitutes a relevant factor in predicting internalized sexual stigma.     

A note on the posterior inference for the Yule–Simon distribution

The Yule–Simon distribution has been out of the radar of the Bayesian community, so far. In this note, we propose an explicit Gibbs sampling scheme when a Gamma prior is chosen for the shape parameter. The performance of the algorithm is illustrated with simulation studies, including count data regression, and a real data application to text analysis. We compare our proposal to the frequentist counterparts showing better performance of our algorithm when a small sample size is considered.     

The Ex-Ante Evaluation of Achieving Sustainable Development Goals

This paper describes the methodology and main results from an overall assessment on future achievement of sustainable development goals. The proposed approach consists of a model-based, looking forward composite sustainable development index—FEEM sustainability index—projected to the future. It represents a first experiment to reproduce the future dynamics of sustainable development indicators over time and worldwide and to assess future sustainability under different scenarios. The assessment presented here is relevant under different viewpoints. First, it has a very broad nature in terms of both geographical coverage and meaningfulness: it considers the multi-dimensional structure of sustainable development by combining relevant indicators belonging to economic, social and environmental pillars for the whole world. Second, the modelling framework to compute future trends of indicators relies upon a recursive-dynamic computable general equilibrium model. This is an ideal tool to look simultaneously at the development of many indicators, their potential interactions and trade-offs, and more in general to the consequences of economic development and/or policies aiming to increase performance in one or more indicators; it allows measuring the overall sustainability under alternative scenarios, across countries and over time. Finally, regarding the construction of the composite indicator, the application of fuzzy measures and Choquet integral increases substantially the model capability allowing taking into account the interactions that exist among the three main pillars of sustainability and the considered indicators.     

The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls

Background

Real-world fall events objectively measured by body-worn sensors can improve the understanding of fall events in older people. However, these events are rare and hence challenging to capture. Therefore, the FARSEEING (FAll Repository for the design of Smart and sElf-adaptive Environments prolonging Independent livinG) consortium and associated partners started to build up a meta-database of real-world falls.

Results

Between January 2012 and December 2015 more than 300 real-world fall events have been recorded. This is currently the largest collection of real-world fall data recorded with inertial sensors. A signal processing and fall verification procedure has been developed and applied to the data. Since the end of 2015, 208 verified real-world fall events are available for analyses. The fall events have been recorded within several studies, with different methods, and in different populations. All sensor signals include at least accelerometer measurements and 58 % additionally include gyroscope and magnetometer measurements. The collection of data is ongoing and open to further partners contributing with fall signals. The FARSEEING consortium also aims to share the collected real-world falls data with other researchers on request.

Conclusions

The FARSEEING meta-database will help to improve the understanding of falls and enable new approaches in fall risk assessment, fall prevention, and fall detection in both aging and disease.

     

Tax differentiation,lobbying, and welfare

To what extent a taxing authority should be granted the power to impose different tax schedules to different groups of taxpayers? Although the policy maker aims at maximizing social welfare, her tax policy may be distorted by the lobbying activity of taxpayers. In this political environment we characterize the conditions under which social welfare can be increased by restricting the set of tax instruments available to the policy maker; i.e., the scope of tax differentiation. We show that full differentiation is more costly, in terms of welfare distortions, when the lobbies are asymmetric in size, while minimal differentiation is more costly when the tax bases are asymmetric across different groups.