True Parameters Research Articles

Equivalence Test on the Protein Content and Quality of Transgenic and Non-Transgenic Soybean Flour

The annual soybean demand in Indonesia reaches 3.09 million ton, with 81% of which are fulfilled through import that mostly consist of transgenic soybean. This condition has become a food safety issue, especially related to the protein content and quality. The purpose of this study was to evaluate the equivalence of protein content and quality of Grobogan local soybean flour (GLSF) compared to transgenic imported soybean flour (TISF) and non-transgenic imported soybean flour (NTSF). The research design was using completely randomized design with 5 replicates (rats) for each treatments. The protein content equivalence test was carried out using the Kjeldhal method, while the protein quality equivalence test was carried out with experimental rats, namely based on the growth method and the nitrogen balance method. Five groups of Sprague Dawley male albino rats, each consisted of 5 rats, were given feeds which contained 10% protein respectively from GLSF, TISF, NTSF, casein as control and a non-protein group. The observationt was carried out for 28 days. The results showed that GLSF had the highest protein content (49.7%db). The equivalence test of protein quality based on growth method showed no significant difference (p>0.05) in feed conversion efficiency, protein efficiency ratio and net protein ratio parameters. The similar test which was based on nitrogen balance method also showed no significant difference (p>0.05) in true protein digestibility, biological value and net protein utilisation parameters between the types of soybean flour. It can be concluded that although the highest protein content was found in GLSF, the three soybean flour types are equivalent in terms of protein quality.

Ultrafast laser ablation, intrinsic threshold, and nanopatterning of monolayer molybdenum disulfide

Laser direct writing is an attractive method for patterning 2D materials without contamination. Literature shows that the ultrafast ablation threshold of graphene across substrates varies by an order of magnitude. Some attribute it to the thermal coupling to the substrates, but it remains by and large an open question. For the first time the effect of substrates on the femtosecond ablation of 2D materials is studied using MoS2 as an example. We show unambiguously that femtosecond ablation of MoS2 is an adiabatic process with negligible heat transfer to the substrates. The observed threshold variation is due to the etalon effect which was not identified before for the laser ablation of 2D materials. Subsequently, an intrinsic ablation threshold is proposed as a true threshold parameter for 2D materials. Additionally, we demonstrate for the first time femtosecond laser patterning of monolayer MoS2 with sub-micron resolution and mm/s speed. Moreover, engineered substrates are shown to enhance the ablation efficiency, enabling patterning with low-power ultrafast oscillators. Finally, a zero-thickness approximation is introduced to predict the field enhancement with simple analytical expressions. Our work clarifies the role of substrates on ablation and firmly establishes ultrafast laser ablation as a viable route to pattern 2D materials.

A closed-form solution for the stochastic volatility model with applications on international stock markets

This paper proposes a closed-form estimator for the stochastic volatility (SV) model. Compared to the usual maximum likelihood estimation (MLE), which is difficult to perform without appropriate approximations, the proposed method can be easily implemented and does not require the use of any numerical optimizer or starting values for iterations. Moreover, closed-form estimates can be supplied as initial values to MLE, for instance, conducted with a novel Laplace approximation. Denoted by MLE-C, this method consistently outperforms other estimators including the Markov chain Monte Carlo (MCMC). This is confirmed with simulation studies consisting of various combinations of true parameters and sample sizes. Our empirical data include daily returns of S&P 500, Nikkei 225 and DAX 100 over 2011–2020. The SV model estimated by MLE-C almost uniformly beats the popular GARCH counterparty, based on both the in-sample fit and out-of-sample forecasting criteria. Value-at-Risk analyses further demonstrate the capability of the SV model to accurately describe the tail behaviors of negative returns.

Passive acoustic monitoring provides predictable and reliable underestimates of population size and longevity in wild Savannah Sparrows

Abstract Many breeding birds produce conspicuous sounds, providing tremendous opportunities to study free-living birds through acoustic recordings. Traditional methods for studying population size and demographic features depend on labor-intensive field research. Passive acoustic monitoring provides an alternative method for quantifying population size and demographic parameters, but this approach requires careful validation. To determine the accuracy of passive acoustic monitoring for estimating population size and demographic parameters, we used autonomous recorders to sample an island-living population of Savannah Sparrows (Passerculus sandwichensis) over a 6-year period. Using the individually distinctive songs of males, we estimated the male population size as the number of unique songs detected in the recordings. We analyzed songs across 6 years to estimate birth year, death year, and longevity. We then compared the estimates with field data in a blind analysis. Estimates of male population size through passive acoustic monitoring were, on average, 72% of the true male population size, with higher accuracy in lower-density years. Estimates of demographic rates were lower than true values by 29% for birth year, 23% for death year, and 29% for longevity. This is the first investigation to estimate longevity with passive acoustic monitoring and adds to a growing number of studies that have used passive acoustic monitoring to estimate population size. Although passive acoustic monitoring underestimated true population parameters, likely due to the high similarity among many male songs, our findings suggest that autonomous recorders can provide reliable estimates of population size and longevity in a wild songbird.

Multi-Fidelity Surrogate-Based Process Mapping with Uncertainty Quantification in Laser Directed Energy Deposition.

A multi-fidelity (MF) surrogate involving Gaussian processes (GPs) is used for designing temporal process maps in laser directed energy deposition (L-DED) additive manufacturing (AM). Process maps are used to establish relationships between the melt pool properties (e.g., melt pool depth) and process parameters (e.g., laser power and scan velocity). The MFGP surrogate involves a high-fidelity (HF) and a low-fidelity (LF) model. The Autodesk Netfabb finite element model (FEM) is selected as the HF model, while an analytical model developed by Eagar-Tsai is chosen as the LF one. The results show that the MFGP surrogate is capable of successfully blending the information present in different fidelity models for designing the temporal forward process maps (e.g., given a set of process parameters for which the true depth is not known, what would be the melt pool depth?). To expand the newly-developed formulation for establishing the temporal inverse process maps (e.g., to achieve the desired melt pool depth for which the true process parameters are not known, what would be the optimal prediction of the process parameters as a function of time?), a case study is performed by coupling the MFGP surrogate with Bayesian Optimization (BO) under computational budget constraints. The results demonstrate that MFGP-BO can significantly improve the optimization solution quality compared to the single-fidelity (SF) GP-BO, along with incurring a lower computational budget. As opposed to the existing methods that are limited to developing steady-state forward process maps, the current work successfully demonstrates the realization of temporal forward and inverse process maps in L-DED incorporating uncertainty quantification (UQ).

Bayesian inference of scaled versus fractional Brownian motion

We present a Bayesian inference scheme for scaled Brownian motion, and investigate its performance on synthetic data for parameter estimation and model selection in a combined inference with fractional Brownian motion. We include the possibility of measurement noise in both models. We find that for trajectories of a few hundred time points the procedure is able to resolve well the true model and parameters. Using the prior of the synthetic data generation process also for the inference, the approach is optimal based on decision theory. We include a comparison with inference using a prior different from the data generating one.

On the Bias in Confirmatory Factor Analysis When Treating Discrete Variables as Ordinal Instead of Continuous

Confirmatory factor analysis is some of the most widely used statistical techniques in the social sciences. Frequently, variables (i.e., items) stemming from questionnaires are analyzed. Two competing approaches for estimating confirmatory factor analysis can be distinguished. First, ordinal variables could be treated as in the case of continuous variables using Pearson correlations, and maximum likelihood estimation method would be applied. Second, an ordinal factor analysis based on polychoric correlations can be fitted. In the majority of the psychometric literature, there is a preference for the ordinal factor analysis based on polychoric correlations because the continuous treatment of variables results in biased factor loadings and biased factor correlations. This article argues that it is not legitimate to speak about bias when comparing the two competing factor analytic approaches because it depends on how true model parameters are defined. This decision can be made individually by a researcher. It is shown in simulation studies and analytical derivations that treating variables ordinally using polychoric correlations instead of continuous using Pearson correlations can also lead to biased estimates of factor loadings and factor correlations. Consequently, it should only be stated that different model parameters are defined in a continuous and an ordinal treatment, and one approach should not generally be preferred over the other.

Addressing Endogeneity Without Instrumental Variables: An Evaluation of the Gaussian Copula Approach for Management Research

The availability and quality of instrumental variables (IV) are frequent concerns in empirical management research when trying to overcome endogeneity problems. For endogeneity that does not arise from sample selection, management scholars have recently started to apply the Gaussian Copula (GC) approach as an alternative to IV regression. Although the GC approach has various promising features, its limitations and usefulness in a management context are still not fully understood. We discuss the GC approach as a flexible, instrument-free approach to correct for endogeneity and examine its suitability for applied management research. We use simulations to explore the limitations and practical usefulness of the GC approach relative to ordinary least squares (OLS), IV regression, and a Higher Moments (HM) estimator by simulating the impact of different degrees of violation of the key underlying assumptions of the GC approach. We show that the GC approach can recover the true parameters remarkably well if all of its assumptions are met but that its absolute and relative performance in terms of parameter recovery and estimation precision can deteriorate quickly if these assumptions are violated. This is of particular concern as some of these assumptions are not testable and violations of them are likely in many empirical management contexts. Based on our results, we provide a series of recommendations and practical guidelines for scholars who consider using the GC approach when dealing with endogeneity.

Targeted principal components regression

We propose a principal components regression method based on maximizing a joint pseudo-likelihood for responses and predictors. Our method uses both responses and predictors to select linear combinations of the predictors relevant for the regression, thereby addressing an oft-cited deficiency of conventional principal components regression. The proposed estimator is shown to be consistent in a wide range of settings, including ones with non-normal and dependent observations; conditions on the first and second moments suffice if the number of predictors (p) is fixed, the number of observations (n) tends to infinity, and dependence is weak, while stronger distributional assumptions are needed when p→∞ with n. We obtain the estimator’s asymptotic distribution as the projection of a multivariate normal random vector onto a tangent cone of the parameter set at the true parameter, and find the estimator is asymptotically more efficient than competing ones. In simulations our method is substantially more accurate than conventional principal components regression and compares favorably to partial least squares and predictor envelopes. The method’s practical usefulness is illustrated in a data example with cross-sectional prediction of stock returns.

CPNCoverageAnalysis: An R package for parameter estimation in conceptual properties norming studies.

In conceptual properties norming studies (CPNs), participants list properties that describe a set of concepts. From CPNs, many different parameters are calculated, such as semantic richness. A generally overlooked issue is that those values are only point estimates of the true unknown population parameters. In the present work, we present an R package that allows us to treat those values as population parameter estimates. Relatedly, a general practice in CPNs is using an equal number of participants who list properties for each concept (i.e., standardizing sample size). As we illustrate through examples, this procedure has negative effects on data's statistical analyses. Here, we argue that a better method is to standardize coverage (i.e., the proportion of sampled properties to the total number of properties that describe a concept), such that a similar coverage is achieved across concepts. When standardizing coverage rather than sample size, it is more likely that the set of concepts in a CPN all exhibit a similar representativeness. Moreover, by computing coverage the researcher can decide whether the CPN reached a sufficiently high coverage, so that its results might be generalizable to other studies. The R package we make available in the current work allows one to compute coverage and to estimate the necessary number of participants to reach a target coverage. We show this sampling procedure by using the R package on real and simulated CPN data.

Bias results for nondifferential mismeasurement of a binary confounder

Suppose we want to estimate the causal effect of an exposure on an outcome, while adjusting for a binary confounder. Suppose that the confounder is measured with error, but that the measurement error is nondifferential. We show that, under certain assumptions, adjusting for the mismeasured confounder produces a biased parameter that lies between the corresponding true and crude parameters. We further show how these assumptions can be tested empirically. We finally show that the bias when adjusting for the mismeasured confounder decreases with the sensitivity and specificity of the mismeasured confounder, provided that the sum of the sensitivity and specificity is at least one. These results have been shown previously for binary exposures and for specific effect measures. We generalize the results for exposures of any class (e.g. continuous and multilevel categorical) and for a wide range of effect measures that includes, but is not limited to, those considered previously.

Online Three-Axis Magnetometer Hard-Iron and Soft-Iron Bias and Angular Velocity Sensor Bias Estimation Using Angular Velocity Sensors for Improved Dynamic Heading Accuracy

This article addresses the problem of dynamic online estimation and compensation of hard-iron and soft-iron biases of three-axis magnetometers under dynamic motion in field robotics, utilizing only biased measurements from a three-axis magnetometer and a three-axis angular rate sensor. The proposed magnetometer and angular velocity bias estimator (MAVBE) utilizes a 15-state process model encoding the nonlinear process dynamics for the magnetometer signal subject to angular velocity excursions, while simultaneously estimating nine magnetometer bias parameters and three angular rate sensor bias parameters, within an extended Kalman filter framework. Bias parameter local observability is numerically evaluated. The bias-compensated signals, together with three-axis accelerometer signals, are utilized to estimate bias-compensated magnetic geodetic heading. Performance of the proposed MAVBE method is evaluated in comparison to the widely cited magnetometer-only TWOSTEP method in numerical simulations, laboratory experiments, and full-scale field trials of an instrumented autonomous underwater vehicle in the Chesapeake Bay, Maryland, USA. For the proposed MAVBE, (i) instrument attitude is not required to estimate biases, and the results show that (ii) the biases are locally observable, (iii) the bias estimates converge rapidly to true bias parameters, (iv) only modest instrument excitation is required for bias estimate convergence, and (v) compensation for magnetometer hard-iron and soft-iron biases dramatically improves dynamic heading estimation accuracy.

Singhing with confidence: visualising the performance of confidence procedures

Confidence intervals are an established means of portraying uncertainty about an inferred parameter and can be generated through the use of confidence distributions. For a confidence distribution to be valid, it must maintain frequentist coverage of the true parameter. This can be represented for a precise distribution by adherence to a cumulative unit uniform distribution, a comparison that is referred to here as a Singh plot. This article extends this to imprecise confidence procedures with bounds around the uniform distribution, and describes how deviations convey information regarding the characteristics of confidence procedures designed for inference and prediction. An example of how Singh plots can highlight deficiencies in proposed methods is given with the ProUCL Chebyshev upper confidence limit estimator for the mean of an unknown distribution. Singh plots can quickly inform the appropriate use of procedures such as these and aid the development of novel confidence procedures to avoid similar errors.

Asymmetric linkages between public-private partnership, environmental innovation, and transport emissions

No one denied the significant role of transportation infrastructure in economic development; however, it is also considered a major contributor to global warming. Therefore, to achieve sustainable development, it is imperative to transform the transport sector investment into low-carbon transportation choices. The public-private partnership investment in the transport sector could be proved a suitable tool for sustainable transport and efficiency. Therefore, we investigate the significance of public-private partnership investment (PPP) on transport emissions by incorporating the role of environmental innovation, economic growth, and population. Our preliminary findings confirm that all variables are abnormally distributed and embodied with structural breaks that distort true parameters in traditional regression. In compliance, we employ Quantile ARDL (QARDL) technique which provides more reliable results in case of non-normality, dynamic trends, and structural changes in data. The long-run empirical outcomes indicate that PPP reduces transport emissions mainly from middle (40th) to highest (95th) emissions quantiles. Similarly, environmental innovation reduces transport emissions from 60th to 95th quantiles. These results imply that the emissions-mitigating effect of PPP and environmental innovation is only pronounced at higher levels of transport emissions. The results provide valuable policy recommendations.

Understanding Implicit Regularization in Over-Parameterized Single Index Model

In this article, we leverage over-parameterization to design regularization-free algorithms for the high-dimensional single index model and provide theoretical guarantees for the induced implicit regularization phenomenon. Specifically, we study both vector and matrix single index models where the link function is nonlinear and unknown, the signal parameter is either a sparse vector or a low-rank symmetric matrix, and the response variable can be heavy-tailed. To gain a better understanding of the role played by implicit regularization without excess technicality, we assume that the distribution of the covariates is known a priori. For both the vector and matrix settings, we construct an over-parameterized least-squares loss function by employing the score function transform and a robust truncation step designed specifically for heavy-tailed data. We propose to estimate the true parameter by applying regularization-free gradient descent to the loss function. When the initialization is close to the origin and the stepsize is sufficiently small, we prove that the obtained solution achieves minimax optimal statistical rates of convergence in both the vector and matrix cases. In addition, our experimental results support our theoretical findings and also demonstrate that our methods empirically outperform classical methods with explicit regularization in terms of both l 2 -statistical rate and variable selection consistency. Supplementary materials for this article are available online.

An Improved Protocol for Verifiable Polynomial Cloud Outsourcing Computation

The cloud computing industry is booming, which involves a large amount of data transmission and calculation, and polynomial calculation is the most basic calculation. Since polynomial calculations have extremely high requirements on the computing power of computing equipment, in most cases, the computing equipment in the user’s hands cannot meet the computing requirements. The user needs to calculate but has no way to do it by himself. In this case, the computing task must be handed over to the cloud server. Aiming at the untrustworthy problem of the cloud server environment, an improved verifiable polynomial cloud outsourcing computing protocol scheme based on the previous outsourcing computing protocol is proposed to ensure the security of the computing process, the privacy of the computing content, and the verifiability of the computing results. This solution hides all the calculation parameters behind the deformed encrypted parameters, so the cloud server cannot obtain the user’s true parameters during the calculation process, and the calculation results can be verified by the user on the client with minimal computing power.

Deep-learning-based post-process correction of the aerosol parameters in the high-resolution Sentinel-3 Level-2 Synergy product

Abstract. Satellite-based aerosol retrievals provide global spatially distributed estimates of atmospheric aerosol parameters that are commonly needed in applications such as estimation of atmospherically corrected satellite data products, climate modelling and air quality monitoring. However, a common feature of the conventional satellite aerosol retrievals is that they have reasonably low spatial resolution and poor accuracy caused by uncertainty in auxiliary model parameters, such as fixed aerosol model parameters, and the approximate forward radiative transfer models utilized to keep the computational complexity feasible. As a result, the improvement and reprocessing of the operational satellite data retrieval algorithms would become a tedious and computationally excessive problem. To overcome these problems, we have developed a machine-learning-based post-process correction approach to correct the existing operational satellite aerosol data products. Our approach combines the existing satellite retrieval data and a post-processing step where a machine learning algorithm is utilized to predict the approximation error in the conventional retrieval. With approximation error, we refer to the discrepancy between the true aerosol parameters and the ones retrieved using the satellite data. Our hypothesis is that the prediction of the approximation error with a finite training dataset is a less complex and easier task than the direct, fully learned machine-learning-based prediction in which the aerosol parameters are directly predicted given the satellite observations and measurement geometry. Our approach does not require reprocessing of the satellite retrieval products; it requires only a computationally fast machine-learning-based post-processing step of the existing retrieval product. Our approach is based on neural networks trained based on collocated satellite data and accurate ground-based Aerosol Robotic Network (AERONET) aerosol data. Based on our post-processing approach, we propose a post-process-corrected high-resolution Sentinel-3 Synergy aerosol product, which gives a spectral estimate of the aerosol optical depth at five different wavelengths with a high spatial resolution equivalent to the native resolution of the Sentinel-3 Level-1 data (300 m at nadir). With aerosol data from Sentinel-3A and 3B satellites, we demonstrate that our approach produces high-resolution aerosol data with clearly better accuracy than the operational Sentinel-3 Level-2 Synergy aerosol product, and it also results in slightly better accuracy than the conventional fully learned machine learning approach. We also demonstrate better generalization capabilities of the post-process correction approach over the fully learned approach.

Randomly initialized EM algorithm for two-component Gaussian mixture achieves near optimality in $O(\sqrt{n})$ iterations

We analyze the classical EM algorithm for parameter estimation in the symmetric two-component Gaussian mixtures in d dimensions. We show that, even in the absence of any separation between components, provided that the sample size satisfies n=\Omega(d \log^4 d) , the randomly initialized EM algorithm converges to an estimate in at most O(\sqrt{n}) iterations with high probability, which is at most O((d / n)^{1/4} \log n) in Euclidean distance from the true parameter and within logarithmic factors of the minimax rate of (d / n)^{1/4} . Both the nonparametric statistical rate and the sublinear convergence rate are direct consequences of the zero Fisher information in the worst case. Refined pointwise guarantees beyond worst-case analysis and convergence to the MLE are also shown under mild conditions. This improves the previous result of Balakrishnan, Wainwright, and Yu (2017), which requires strong conditions on both the separation of the components and the quality of the initialization, and that of Daskalakis, Tzamos, and Zampetakis (2017), which requires sample splitting and restarting the EM iteration.

Estimation of Ocean Biogeochemical Parameters in an Earth System Model Using the Dual One Step Ahead Smoother: A Twin Experiment

Ocean biogeochemical (BGC) models utilise a large number of poorly-constrained global parameters to mimic unresolved processes and reproduce the observed complex spatio-temporal patterns. Large model errors stem primarily from inaccuracies in these parameters whose optimal values can vary both in space and time. This study aims to demonstrate the ability of ensemble data assimilation (DA) methods to provide high-quality and improved BGC parameters within an Earth system model in an idealized perfect twin experiment framework. We use the Norwegian Climate Prediction Model (NorCPM), which combines the Norwegian Earth System Model with the Dual-One-Step ahead smoothing-based Ensemble Kalman Filter (DOSA-EnKF). We aim to estimate five spatially varying BGC parameters by assimilating salinity and temperature profiles and surface BGC (Phytoplankton, Nitrate, Phosphate, Silicate, and Oxygen) observations in a strongly coupled DA framework—i.e., jointly updating ocean and BGC state-parameters during the assimilation. We show how BGC observations can effectively constrain error in the ocean physics and vice versa. The method converges quickly (less than a year) and largely reduces the errors in the BGC parameters. Some parameter error remains, but the resulting state variable error using the estimated parameters for a free ensemble run and for a reanalysis performs nearly as well as with true parameter values. Optimal parameter values can also be recovered by assimilating climatological BGC observations or sparse observational networks. The findings of this study demonstrate the applicability of the DA approach for tuning the system in a real framework.

Evaluating the robustness of targeted maximum likelihood estimators via realistic simulations in nutrition intervention trials.

Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targeted maximum likelihood estimation. There are even more recent augmentations of these procedures that can increase robustness, by adding a layer of cross-validation (cross-validated targeted maximum likelihood estimation and double machine learning, as applied to substitution and estimating equation approaches, respectively). While these methods have been evaluated individually on simulated and experimental data sets, a comprehensive analysis of their performance across real data based simulations have yet to be conducted. In this work, we benchmark multiple widely used methods for estimation of the average treatment effect using ten different nutrition intervention studies data. A nonparametric regression method, undersmoothed highly adaptive lasso, is used to generate the simulated distribution which preserves important features from the observed data and reproduces a set of true target parameters. For each simulated data, we apply the methods above to estimate the average treatment effects as well as their standard errors and resulting confidence intervals. Based on the analytic results, a general recommendation is put forth for use of the cross-validated variants of both substitution and estimating equation estimators. We conclude that the additional layer of cross-validation helps in avoiding unintentional over-fitting of nuisance parameter functionals and leads to more robust inferences.