Deductive semiparametric estimation in Double-Sampling Designs with application to PEPFAR

Non-ignorable dropout is common in studies with long follow-up time, and it can bias study results unless handled carefully. A double-sampling design allocates additional resources to pursue a subsample of the dropouts and find out their outcomes, which can address potential biases due to non-ignorable dropout. It is desirable to construct semiparametric estimators for the double-sampling design because of their robustness properties. However, obtaining such semiparametric estimators remains a challenge due to the requirement of the analytic form of the efficient influence function (EIF), the derivation of which can be ad hoc and difficult for the double-sampling design. Recent work has shown how the derivation of EIF can be made deductive and computerizable using the functional derivative representation of the EIF in nonparametric models. This approach, however, requires deriving the mixture of a continuous distribution and a point mass, which can itself be challenging for complicated problems such as the double-sampling design. We propose semiparametric estimators for the survival probability in double-sampling designs by generalizing the deductive and computerizable estimation approach. In particular, we propose to build the semiparametric estimators based on a discretized support structure, which approximates the possibly continuous observed data distribution and circumvents the derivation of the mixture distribution. Our approach is deductive in the sense that it is expected to produce semiparametric locally efficient estimators within finite steps without knowledge of the EIF. We apply the proposed estimators to estimating the mortality rate in a double-sampling design component of the President's Emergency Plan for AIDS Relief (PEPFAR) program. We evaluate the impact of double-sampling selection criteria on the mortality rate estimates

Linear mixed models with endogenous covariates: modeling sequential treatment effects with application to a mobile health study

Mobile health is a rapidly developing field in which behavioral treatments are delivered to individuals via wearables or smartphones to facilitate health-related behavior change. Micro-randomized trials (MRT) are an experimental design for developing mobile health interventions. In an MRT the treatments are randomized numerous times for each individual over course of the trial. Along with assessing treatment effects, behavioral scientists aim to understand between-person heterogeneity in the treatment effect. A natural approach is the familiar linear mixed model. However, directly applying linear mixed models is problematic because potential moderators of the treatment effect are frequently endogenous---that is, may depend on prior treatment. We discuss model interpretation and biases that arise in the absence of additional assumptions when endogenous covariates are included in a linear mixed model. In particular, when there are endogenous covariates, the coefficients no longer have the customary marginal interpretation. However, these coefficients still have a conditional-on-the-random-effect interpretation. We provide an additional assumption that, if true, allows scientists to use standard software to fit linear mixed model with endogenous covariates, and person-specific predictions of effects can be provided. As an illustration, we assess the effect of activity suggestion in the HeartSteps MRT and analyze the between-person treatment effect heterogeneity

The spatial accessibility of attractive parks in Chicago and a proposed planning support system to evaluate the accessibility of POIs

Urban parks play an essential role in meeting the ecological, social, and recreational requirements of residents. Access to urban parks reflects people's quality of life. The present research focused on the cumulative accessibility by walking and driving to attractive urban parks for different population groups in Chicago. The present study used the ratings and the number of reviews on Google Maps to evaluate each parks' attractiveness. The results present the cumulative accessibility scores using gravity, linear, and kernel models. In addition, the spatial distribution of the accessibility to parks for population groups of different races and levels of income are shown at the 90 m X 90 m land cell scale and at the community level. Highly attractive parks that people walk to receive a high accessibility score. However, parks with high accessibility scores that people drive to were along the major highways. The present study also determined that the Black and high-income populations have a higher accessibility score to parks than other population groups. Moreover, a planning supporting system is proposed that uses rating data that can be gathered from apps such as Google Maps and Yelp to evaluate all types of Points of Interest (POIs) in parks

Maximum Likelihood Estimation of Parameters in Exponential Power Distribution with Upper Record Values

The exponential power (EP) distribution is a very important distribution that was used by survival analysis and related with asymmetrical EP distribution. Many researchers have discussed statistical inference about the parameters in EP distribution using i.i.d random samples. However, sometimes available data might contain only record values, or it is more convenient for researchers to collect record values. We aim to resolve this problem. We estimated two parameters of the EP distribution by MLE using upper record values. According to simulation study, we used the Bias and MSE of the estimators for studying the efficiency of the proposed estimation method. Then, we discussed the prediction on the next upper record value by known upper record values. The study concluded that MLEs of EP distribution parameters by upper record values has satisfactory performance. Also, prediction of the next upper record value performed wel

A novel entropy production based full-chip TSV fatigue analysis

Through-silicon vias (TSVs) are subject to thermal fatigue due to stress over time, no matter how small the stress is. Existing works on TSV fatigue all rely on measurement-based parameters to estimate the lifetime, and cannot consider detailed thermal profiles. In this paper, we propose a new method for TSV fatigue prediction using entropy production during thermal cycles. By combining thermodynamics and mechanics laws, the fatigue process can be quantitatively evaluated with detailed thermal profiles. Experimental results show that interestingly, the landing pad possesses the most easy-to-fail region, which generates up to 50% more entropy compared with the TSV body. The impact of landing pad dimension and TSV geometries are also studied, providing guidance for reliability enhancement. Finally, full-chip fatigue analysis is performed based on stress superposition. To the best of the authors\u27 knowledge, this is the first TSV fatigue model that is free of measurement data fitting, the first that is capable of considering detailed thermal profiles, and the first framework for efficient full-chip TSV fatigue analysis. --Abstract, page iii

Natural evolution strategies and variational Monte Carlo

A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization. Recent work of Gomes et al. [2019] on heuristic combinatorial optimization using neural quantum states is pedagogically reviewed in this context, emphasizing the connection with natural evolution strategies. The algorithmic framework is illustrated for approximate combinatorial optimization problems, and a systematic strategy is found for improving the approximation ratios. In particular it is found that natural evolution strategies can achieve approximation ratios competitive with widely used heuristic algorithms for Max-Cut, at the expense of increased computation time

Chemically enhanced primary treatment processes for wastewater resource redirection and its impact on downstream processes

There is a recent focus within the global wastewater industry on the steps being taken by treatment facilities to move towards net zero, or in some cases, energy positive operation. As part of maximizing energy recovery from the incoming wastewater, there has been increased attention upon the energy contained in the wastewater, and maximizing the redirection of more carbon captured through the primary treatment process rather than conventional removal through carbon oxidation. The chemically enhanced primary treatment (CEPT) process is a promising method for carbon redirection and improving the performance and efficiency of wastewater treatment processes. This research was conducted to optimize the CEPT performance regarding simultaneous carbon and nutrients redirection in both bench-scale and full-scale operations. In order to improve the CEPT process, the performance of ferric chloride and seven types of polymers were evaluated by jar tests. Results indicated that 15 mg/L ferric chloride and 0.5 mg/L poly aluminum chloride (PACL) showed the best performance which was determined by a simplified comparison matrix regarding removal efficiencies. The best coagulant and flocculant combination determined by this study achieved total chemical oxygen demand (tCOD), soluble chemical oxygen demand (sCOD), total suspended solids (TSS) and total phosphorus (TP) removal efficiencies of 76%, 58%, 89%, and 84%, respectively, in a full-scale primary clarifier operation. Furthermore, the relationship between influent characteristics and removal rates under varying operating conditions were investigated. The impact of CEPT on the downstream liquid and solid train processes were also investigated. The study on the impact of CEPT on the downstream liquid train processes showed that PACl addition has improved the SVI in the activated sludge process, and lowered TSS and TP concentrations in secondary clarifier effluent. Furthermore, the addition of PACl did not affect the BOD5 and the ammonia concentration of the effluent from the secondary clarifier. However, the sludge produced from CEPT dosed with ferric chloride and PACl (test clarifier sludge) showed a lesser methane production rate compared to the sludge produced from CEPT dosed with ferric chloride (control clarifier sludge)