A generalization of moderated statistics to data adaptive semiparametric estimation in high-dimensional biology

The widespread availability of high-dimensional biological data has made the simultaneous screening of numerous biological characteristics a central statistical problem in computational biology. While the dimensionality of such datasets continues to increase, the problem of teasing out the effects of biomarkers in studies measuring baseline confounders while avoiding model misspecification remains only partially addressed. Efficient estimators constructed from data adaptive estimates of the data-generating distribution provide an avenue for avoiding model misspecification; however, in the context of high-dimensional problems requiring simultaneous estimation of numerous parameters, standard variance estimators have proven unstable, resulting in unreliable Type-I error control under standard multiple testing corrections. We present the formulation of a general approach for applying empirical Bayes shrinkage approaches to asymptotically linear estimators of parameters defined in the nonparametric model. The proposal applies existing shrinkage estimators to the estimated variance of the influence function, allowing for increased inferential stability in high-dimensional settings. A methodology for nonparametric variable importance analysis for use with high-dimensional biological datasets with modest sample sizes is introduced and the proposed technique is demonstrated to be robust in small samples even when relying on data adaptive estimators that eschew parametric forms. Use of the proposed variance moderation strategy in constructing stabilized variable importance measures of biomarkers is demonstrated by application to an observational study of occupational exposure. The result is a data adaptive approach for robustly uncovering stable associations in high-dimensional data with limited sample sizes

Revisiting the propensity score's central role: Towards bridging balance and efficiency in the era of causal machine learning

About forty years ago, in a now--seminal contribution, Rosenbaum & Rubin (1983) introduced a critical characterization of the propensity score as a central quantity for drawing causal inferences in observational study settings. In the decades since, much progress has been made across several research fronts in causal inference, notably including the re-weighting and matching paradigms. Focusing on the former and specifically on its intersection with machine learning and semiparametric efficiency theory, we re-examine the role of the propensity score in modern methodological developments. As Rosenbaum & Rubin (1983)'s contribution spurred a focus on the balancing property of the propensity score, we re-examine the degree to which and how this property plays a role in the development of asymptotically efficient estimators of causal effects; moreover, we discuss a connection between the balancing property and efficient estimation in the form of score equations and propose a score test for evaluating whether an estimator achieves balance.Comment: Accepted for publication in a forthcoming special issue of Observational Studie

Benefits of greenhouse gas mitigation on the supply, management, and use of water resources in the United States

Climate change impacts on water resources in the United States are likely to be far-reaching and substantial because the water is integral to climate, and the water sector spans many parts of the economy. This paper estimates impacts and damages from five water resource-related models addressing runoff, drought risk, economics of water supply/demand, water stress, and flooding damages. The models differ in the water system assessed, spatial scale, and unit of assessment, but together provide a quantitative and descriptive richness in characterizing water sector effects that no single model can capture. The results, driven by a consistent set of greenhouse gas (GHG) emission and climate scenarios, examine uncertainty from emissions, climate sensitivity, and climate model selection. While calculating the net impact of climate change on the water sector as a whole may be impractical, broad conclusions can be drawn regarding patterns of change and benefits of GHG mitigation. Four key findings emerge: 1) GHG mitigation substantially reduces hydro-climatic impacts on the water sector; 2) GHG mitigation provides substantial national economic benefits in water resources related sectors; 3) the models show a strong signal of wetting for the Eastern US and a strong signal of drying in the Southwest; and 4) unmanaged hydrologic systems impacts show strong correlation with the change in magnitude and direction of precipitation and temperature from climate models, but managed water resource systems and regional economic systems show lower correlation with changes in climate variables due to non-linearities created by water infrastructure and the socio-economic changes in non-climate driven water demand

Development of constitutive model for precast prestressed concrete segmental columns

The interest of using precast segmental columns in construction of concrete bridges has significantly increased in recent years. One research area of concrete bridges is the application of Precast Prestressed Concrete Segmental (PPCS) Column in any structural analysis software or FE program code. Modeling a PPCS column, which consists of various materials with interaction between them, is complicated and time-consuming. This research attempts to formulate the stiffness matrix of PPCS columns in order to form the constitutive model in linear form to evaluate the response of the columns. A two-dimensional finite element model is presented in the finite element package ANSYS. Parametric studies are conducted by finite element models to verify the constitutive models for the PPCS column with a different number of concrete segments. Comparison between the constitutive model and the FE program results indicates that the constitutive model is accurate enough to predict the deformation of the PPCS columns

Laboratory Evaluation of Five Chitin Synthesis Inhibitors Against the Colorado Potato Beetle, Leptinotarsa decemlineata

Results of laboratory experiments are reported that tested the effects of five chitin synthesis inhibitors, diflubenzuron, cyromazine, lufenuron, hexaflumuron and triflumuron. on second instars of the Colorado potato beetle, Leptinotarsa decemlineata (Say) (Coleoptera: Crysomelidae), originally collected from potato fields of Bostanabaad, a town 66 km southeast of Tabriz, Iran. In bioassays, the larvae were fed potato leaves dipped in aqueous solutions containing chitin synthesis inhibitors. The mortalities and abnormalities of the treated larvae were recorded 72 hours after treatments. LC50 values were 58.6, 69.6, 27.3, 0.79 and 81.4 mg ai/ L for diflubenzuron, cyromazine, lufenuron, hexaflumuron and triflumuron, respectively. Compared with phosalone, which is one of the common insecticides used for controlling this pest in Iran, lufenuron and hexaflumuron seem to be much more potent, and if they perform equally well in the field, they would be suitable candidates to be considered as reduced risk insecticides in management programs for L. decemlineata due to much wider margin of safety for mammals and considerably fewer undesirable environmental side effects

Toxicity and side effects of three insecticides on adult Chrysoperla carnea (Neu.: Chrysopidae) under laboratory conditions

Green lacewing, Chrysoperla carnea (Stephens), is an important predator of arthropod pests such as aphids, psyllids, thrips and whiteflies. Toxicity of endosulfan, imidacloprid and indoxacarb was assessed on male and female C. carnea in the laboratory. Contact bioassays were carried out in glass Petri dishes. The LC50 values for indoxacarb, imidacloprid and endosulfan were 0.011, 0.053, and 0.343 g AI/L for males, and 0.019, 0.098 and 0.398 g AI/L for females, respectively. Males were more sensitive than females to all three insecticides. To assess the sublethal effects, using IOBC (International Organization for Biological Control) method, adults were treated with LC25 of each insecticide. Analysis of variance did not show significant differences among treatments regarding the developmental time of the first, second and third instars, pupae and sex ratio. Differences between treatments and control were significant regarding pre-oviposition, oviposition and post-oviposition periods, fecundity, fertility, longevity of male and female. Mean longevity for control, imidacloprid, endosulfan and indoxacarb were 30 ± 2.3, 24.3 ± 3.3, 21.3 ± 2.4 and 19.7 ± 1.4 days for males, and 36.9 ± 2.5, 31.8 ± 2.9, 27.7 ± 1.7 and 26.7 ± 2.6 days for females, respectively. The highest and the lowest rates of fecundity were 540 ± 49 and 206 ± 42 in control and indoxacarb, respectively. Based on the IOBC classification method, imidacloprid, endosulfan and indoxacarb were slightly harmful (%30 < Total Effect Index < %79) against adults. The adult stage was very sensitive to indoxacarb, imidacloprid and endosulfan. Hence, these insecticides should not be applied when the density of adults is high in the field

The Unseen Face of E-Business Project Development

The purpose of this paper is intent on identify and analyze the unseen factors of successful or failure of e-business project development. The IT managers must take into account both all costs involved in e-business development and all phases (analysis, design, testing, implementation, maintenance and operation) according to principle of project management for software/systems life cycle development. There are many solutions to exceed these factors of failure among could be counted outsourcing, a good project management, involvement of senior management, a real cost estimation etc.Zadanie pt. „Digitalizacja i udostępnienie w Cyfrowym Repozytorium Uniwersytetu Łódzkiego kolekcji czasopism naukowych wydawanych przez Uniwersytet Łódzki” nr 885/P-DUN/2014 zostało dofinansowane ze środków MNiSW w ramach działalności upowszechniającej nauk