Sparse logistic principal components analysis for binary data

We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities of the binary observations. Sparsity is introduced to the principal component (PC) loading vectors for enhanced interpretability and more stable extraction of the principal components. Our sparse PCA is formulated as solving an optimization problem with a criterion function motivated from a penalized Bernoulli likelihood. A Majorization--Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated by application to a single nucleotide polymorphism data set and a simulation study.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS327 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Three essays on Renewable Portfolio Standards

The first essay investigates the technical efficiencies of South Korean solar photovoltaic (PV) power plants by type: ground-mounted PV (GPV) and rooftop PV (BPV). The two-step stochastic frontier analysis (SFA) of the true-random effects model is used to capture heterogeneity. In the results, the first-order input parameters are positive and significant, satisfying the monotonicity condition for valid production functions, except for the daily sunshine hours. The average technical efficiency (TE) scores for BPV and GPV are 0.995 and 0.991, respectively, it can be concluded that there is no evidence that many plants of these types are significantly lagging behind the most efficient producers of the type. The estimates of mean technology gap ratio (TGR) values are very close to 1, and the meta-technology efficiency (MTE) scores are 0.991 for the BPV and 0.985 for the GPV. There is a small difference in TEs, TGRs, and input use. The second essay examines how the Renewable Portfolio Standards (RPS) policy influences the decision-making process of manufacturers regarding the choice between staying in their current country or relocating to a foreign country in response to initiatives such as RE100. Three-stage game is considered in which three player groups participate: the social net benefit maximizing government sets the RPS target in the first stage, the profit maximizing RPS obligors (utilities) makes decisions regarding the amount of renewable electricity they will provide in the second stage, and the profit maximizing firms supplying RE100 companies determine to remain in their current country or relocate to another country offering cheaper renewable electricity in the third stage. The findings indicate that a rational government will choose a target share that maintains employment as long as it brings a non-negative net benefit. Moreover, there exists a range where between domestic and foreign renewable energy prices to determine domestic production, even when domestic prices are higher. By increasing the price of non-renewable electricity, it is possible to subsidize renewable electricity depending on cost transparency. The exogenous variables determine the subgame perfect equilibrium. The third essay investigates the impact of RPS policies on total primary crop acreage in the United States. Our empirical framework is based on the premise that acreage is influenced by climatic factors, farmers' crop management practices, and land allocation decisions, while considering input and expected production prices. We extend the framework to incorporate the influence of renewable electricity policy (RPS) and other agricultural policy (CRP). The coefficients of the composite price index are positively related to acreage, while the coefficients of the fertilizer price index exhibit a negative relationship. The estimated output price elasticities range from 0.297 to 0.329, and the elasticities from the models considering electricity market characteristics show similar magnitudes, approximately 0.30. The RPS electricity supply target is found to significantly reduce acreage, although the actual magnitude of reduction is relatively modest, estimated at around 24 to 26 acres per 1000 MWh. Crop acreage changes by target level of renewable electricity is similar to STRATA's data, however it can be seen as overestimated based on the National Renewable Energy Laboratory (NREL)'s data.Includes bibliographical references

Dust, Ice, and Gas in Time (DIGIT) Herschel Observations of GSS30-IRS1 in Ophiuchus

As a part of the "Dust, Ice, and Gas In Time" (DIGIT) key program on Herschel, we observed GSS30-IRS1, a Class I protostar located in Ophiuchus (d = 120 pc), with Herschel/Photodetector Array Camera and Spectrometer. More than 70 lines were detected within a wavelength range from 50 to 200 mu m, including CO, H2O, OH, and two atomic [O I] lines at 63 and 145 mu m. The [C II] line, known as a tracer of externally heated gas by the interstellar radiation field (ISRF), is also detected at 158 mu m. All lines, except [O I] and [C II], are detected only at the central spaxel of 9 ''.4 x 9 ''.4. The [O I] emissions are extended along a NE-SW orientation, and the [C II] line is detected over all spaxels, indicative of an external photodissociation region. The total [C II] intensity around GSS30 reveals that the far-ultraviolet radiation field is in the range of 3 to 20 G(0), where G(0) is in units of the Habing Field, 1.6 x 10(-3) erg cm(-2) s(-1). This enhanced external radiation field heats the envelope of GSS30-IRS1, causing the continuum emission to be extended, unlike the molecular emission. The best-fit continuum model of GSS30-IRS1 with the physical structure including flared disk, envelope, and outflow shows that the internal luminosity is 10 L-circle dot, and the region is externally heated by a radiation field enhanced by a factor of 130 compared to the standard local ISRF.NASANational Research Foundation of Korea (NRF) - Ministry of Education of the Korean government NRF-2012R1A1A2044689National Research Foundation (NRF) - Ministry of Education of KoreaAstronom

Principal Components Analysis for Binary Data

Principal components analysis (PCA) has been widely used as a statistical tool for the dimension reduction of multivariate data in various application areas and extensively studied in the long history of statistics. One of the limitations of PCA machinery is that PCA can be applied only to the continuous type variables. Recent advances of information technology in various applied areas have created numerous large diverse data sets with a high dimensional feature space, including high dimensional binary data. In spite of such great demands, only a few methodologies tailored to such binary dataset have been suggested. The methodologies we developed are the model-based approach for generalization to binary data. We developed a statistical model for binary PCA and proposed two stable estimation procedures using MM algorithm and variational method. By considering the regularization technique, the selection of important variables is automatically achieved. We also proposed an efficient algorithm for model selection including the choice of the number of principal components and regularization parameter in this study