Happiness and Loss Aversion: When Social Participation Dominates Comparision

A central finding in happiness research is that a person’s income relative to the average income in her social reference group is more important for her life satisfaction than the absolute level of her income. This dependence of life satisfaction on relative income can be related to the reference dependence of the value function in Kahneman and Tversky’s (1979) prospect theory. In this paper we investigate whether the characteristics of the value function like concavity for gains, convexity for losses, and loss aversion apply to the dependence of life satisfaction on relative income. This is tested with a new measure for the reference income for a large German panel for the years 1984-2001. We find concavity of life satisfaction in positive relative income, but unexpectedly strongly significant concavity of life satisfaction in negative relative income as well. The latter result is shown to be robust to extreme distortions of the reported-life-satisfaction scale. It implies a rising marginal sensitivity of life satisfaction to more negative values of relative income, and hence loss aversion (in a wide sense). This may be explained in terms of increasing financial obstacles to social participation.public economics ;

Exploring the Relationship of Relative Telomere Length and the Epigenetic Clock in the LipidCardio Cohort

Telomere length has been accepted widely as a biomarker of aging. Recently, a novel candidate biomarker has been suggested to predict an individual’s chronological age with high accuracy: The epigenetic clock is based on the weighted DNA methylation (DNAm) fraction of a number of cytosine-phosphate-guanine sites (CpGs) selected by penalized regression analysis. Here, an established methylation-sensitive single nucleotide primer extension method was adapted, to estimate the epigenetic age of the 1005 participants of the LipidCardio Study, a patient cohort characterised by high prevalence of cardiovascular disease, based on a seven CpGs epigenetic clock. Furthermore, we measured relative leukocyte telomere length (rLTL) to assess the relationship between the established and the promising new measure of biological age. Both rLTL (0.79 ± 0.14) and DNAm age (69.67 ± 7.27 years) were available for 773 subjects (31.6% female; mean chronological age= 69.68 ± 11.01 years; mean DNAm age acceleration = −0.01 ± 7.83 years). While we detected a significant correlation between chronological age and DNAm age (n = 779, R = 0.69), we found neither evidence of an association between rLTL and the DNAm age (β = 3.00, p = 0.18) nor rLTL and the DNAm age acceleration (β = 2.76, p = 0.22) in the studied cohort, suggesting that DNAm age and rLTL measure different aspects of biological age

Determinants of the population growth of the West Nile virus mosquito vector Culex pipiens in a repeatedly affected area in Italy

Background The recent spread of West Nile Virus in temperate countries has raised concern. Predicting the likelihood of transmission is crucial to ascertain the threat to Public and Veterinary Health. However, accurate models of West Nile Virus (WNV) expansion in Europe may be hampered by limited understanding of the population dynamics of their primary mosquito vectors and their response to environmental changes.<p></p> Methods We used data collected in north-eastern Italy (2009–2011) to analyze the determinants of the population growth rate of the primary WNV vector Culex pipiens. A series of alternative growth models were fitted to longitudinal data on mosquito abundance to evaluate the strength of evidence for regulation by intrinsic density-dependent and/or extrinsic environmental factors. Model-averaging algorithms were then used to estimate the relative importance of intrinsic and extrinsic variables in describing the variations of per-capita growth rates.<p></p> Results Results indicate a much greater contribution of density-dependence in regulating vector population growth rates than of any environmental factor on its own. Analysis of an average model of Cx. pipiens growth revealed that the most significant predictors of their population dynamics was the length of daylight, estimated population size and temperature conditions in the 15 day period prior to sampling. Other extrinsic variables (including measures of precipitation, number of rainy days, and humidity) had only a minor influence on Cx. pipiens growth rates.<p></p> Conclusions These results indicate the need to incorporate density dependence in combination with key environmental factors for robust prediction of Cx. pipiens population expansion and WNV transmission risk. We hypothesize that detailed analysis of the determinants of mosquito vector growth rate as conducted here can help identify when and where an increase in vector population size and associated WNV transmission risk should be expected.<p></p&gt

Variable Selection and Model Choice in Structured Survival Models

In many situations, medical applications ask for flexible survival models that allow to extend the classical Cox-model via the inclusion of time-varying and nonparametric effects. These structured survival models are very flexible but additional difficulties arise when model choice and variable selection is desired. In particular, it has to be decided which covariates should be assigned time-varying effects or whether parametric modeling is sufficient for a given covariate. Component-wise boosting provides a means of likelihood-based model fitting that enables simultaneous variable selection and model choice. We introduce a component-wise likelihood-based boosting algorithm for survival data that permits the inclusion of both parametric and nonparametric time-varying effects as well as nonparametric effects of continuous covariates utilizing penalized splines as the main modeling technique. Its properties and performance are investigated in simulation studies. The new modeling approach is used to build a flexible survival model for intensive care patients suffering from severe sepsis. A software implementation is available to the interested reader

Kernel Partial Correlation Coefficient -- a Measure of Conditional Dependence

In this paper we propose and study a class of simple, nonparametric, yet interpretable measures of conditional dependence between two random variables $Y$ and $Z$ given a third variable $X$, all taking values in general topological spaces. The population version of any of these measures captures the strength of conditional dependence and it is 0 if and only if $Y$ and $Z$ are conditionally independent given $X$, and 1 if and only if $Y$ is a measurable function of $Z$ and $X$. Thus, our measure -- which we call kernel partial correlation (KPC) coefficient -- can be thought of as a nonparametric generalization of the partial correlation coefficient that possesses the above properties when $(X,Y,Z)$ is jointly normal. We describe two consistent methods of estimating KPC. Our first method utilizes the general framework of geometric graphs, including $K$-nearest neighbor graphs and minimum spanning trees. A sub-class of these estimators can be computed in near linear time and converges at a rate that automatically adapts to the intrinsic dimension(s) of the underlying distribution(s). Our second strategy involves direct estimation of conditional mean embeddings using cross-covariance operators in the reproducing kernel Hilbert spaces. Using these empirical measures we develop forward stepwise (high-dimensional) nonlinear variable selection algorithms. We show that our algorithm, using the graph-based estimator, yields a provably consistent model-free variable selection procedure, even in the high-dimensional regime when the number of covariates grows exponentially with the sample size, under suitable sparsity assumptions. Extensive simulation and real-data examples illustrate the superior performance of our methods compared to existing procedures. The recent conditional dependence measure proposed by Azadkia and Chatterjee (2019) can be viewed as a special case of our general framework.Comment: 63 pages, 4 figures, 9 table

Incorporating spatial variation in housing attribute prices: A comparison of geographically weighted regression and the spatial expansion method

Hedonic house price models typically impose a constant price structure on housing characteristics throughout an entire market area. However, there is increasing evidence that the marginal prices of many important attributes vary over space, especially within large markets. In this paper, we compare two approaches to examine spatial heterogeneity in housing attribute prices within the Tucson, Arizona housing market: the spatial expansion method and geographically weighted regression (GWR). Our results provide strong evidence that the marginal price of key housing characteristics varies over space. GWR outperforms the spatial expansion method in terms of explanatory power and predictive accuracy.

Novel proposal for prediction of CO2 course and occupancy recognition in Intelligent Buildings within IoT

Many direct and indirect methods, processes, and sensors available on the market today are used to monitor the occupancy of selected Intelligent Building (IB) premises and the living activities of IB residents. By recognizing the occupancy of individual spaces in IB, IB can be optimally automated in conjunction with energy savings. This article proposes a novel method of indirect occupancy monitoring using CO2, temperature, and relative humidity measured by means of standard operating measurements using the KNX (Konnex (standard EN 50090, ISO/IEC 14543)) technology to monitor laboratory room occupancy in an intelligent building within the Internet of Things (IoT). The article further describes the design and creation of a Software (SW) tool for ensuring connectivity of the KNX technology and the IoT IBM Watson platform in real-time for storing and visualization of the values measured using a Message Queuing Telemetry Transport (MQTT) protocol and data storage into a CouchDB type database. As part of the proposed occupancy determination method, the prediction of the course of CO2 concentration from the measured temperature and relative humidity values were performed using mathematical methods of Linear Regression, Neural Networks, and Random Tree (using IBM SPSS Modeler) with an accuracy higher than 90%. To increase the accuracy of the prediction, the application of suppression of additive noise from the CO2 signal predicted by CO2 using the Least mean squares (LMS) algorithm in adaptive filtering (AF) method was used within the newly designed method. In selected experiments, the prediction accuracy with LMS adaptive filtration was better than 95%.Web of Science1223art. no. 454

Change of support problemへの新たな空間統計モデルの開発

筑波大学 (University of Tsukuba)201

Force-induced acoustic phonon transport across single-digit nanometre vacuum gaps

Heat transfer between bodies separated by nanoscale vacuum gap distances has been extensively studied for potential applications in thermal management, energy conversion and data storage. For vacuum gap distances down to 20 nm, state-of-the-art experiments demonstrated that heat transport is mediated by near-field thermal radiation, which can exceed Planck's blackbody limit due to the tunneling of evanescent electromagnetic waves. However, at sub-10-nm vacuum gap distances, current measurements are in disagreement on the mechanisms driving thermal transport. While it has been hypothesized that acoustic phonon transport across single-digit nanometre vacuum gaps (or acoustic phonon tunneling) can dominate heat transfer, the underlying physics of this phenomenon and its experimental demonstration are still unexplored. Here, we use a custom-built high-vacuum shear force microscope (HV-SFM) to measure heat transfer between a silicon (Si) tip and a feedback-controlled platinum (Pt) nanoheater in the near-contact, asperity-contact, and bulk-contact regimes. We demonstrate that in the near-contact regime (i.e., single-digit nanometre or smaller vacuum gaps before making asperity contact), heat transfer between Si and Pt surfaces is dominated by force-induced acoustic phonon transport that exceeds near-field thermal radiation predictions by up to three orders of magnitude. The measured thermal conductance shows a gap dependence of $d^{-5.7\pm1.1}$ in the near-contact regime, which is consistent with acoustic phonon transport modelling based on the atomistic Green's function (AGF) framework. Our work suggests the possibility of engineering heat transfer across single-digit nanometre vacuum gaps with external force stimuli, which can make transformative impacts to the development of emerging thermal management technologies.Comment: 9 pages with 4 figures (Main text), 13 pages with 7 figures (Methods), and 13 pages with 6 figures and 1 table (Supplementary Information