True Coefficient Research Articles

Oracle inequalities for weighted group Lasso in high-dimensional Poisson regression model

This article considers the problem of estimating the high-dimensional Poisson regression model with group sparsity in the parameter vector using the weighted group Lasso method. Non asymptotic oracle inequalities measuring the convergence rate of the estimation and prediction errors are given, provided that the true regression coefficients and the covariates satisfy suitable conditions. The non asymptotic oracle inequalities imply that the weighted group lasso estimator is consistent. A comparison of the performance of our proposed weights with other adaptive weights in simulated data shows that the weighted group Lasso estimator provides a good sparse approximation to the true parameters.

The Effect of the Second Stage Estimator on Model Performance in Post-LASSO Method

Penalized linear regression methods are used for the accurate prediction of new observations and to obtain interpretable models. The performance of these methods depends on the properties of the true coefficient vector. The LASSO method is a penalized regression method that can simultaneously perform coefficient shrinkage and variable selection in a continuous process. Depending on the structure of the dataset, different estimators have been proposed to overcome the problems faced by LASSO. The estimation method used in the second stage of the post-LASSO two-stage regression method proposed as an alternative to LASSO has a considerable effect on model performance.
 In this study, the performance of the post-LASSO is compared with classical penalized regression methods ridge, LASSO, elastic net, adaptive LASSO and Post-LASSO by using different estimation methods in the second stage of the post-LASSO. In addition, the effect of the magnitude and position of the signal values in the real coefficient vector on the performance of the models obtained by these methods is analyzed. The mean squared error and standard deviation of the predictions calculated on the test set are used to compare the prediction performance of the models, while the active set sizes are used to compare their performance in variable selection. According to the findings obtained from the simulation studies, the choice of the second-stage estimator and the structure of the true coefficient vector significantly affect the success of the post-LASSO method compared to other methods.

Sampling strategy matters to accurately estimate response curves' parameters in species distribution models

AbstractAimAssessing how different sampling strategies affect the accuracy and precision of species response curves estimated by parametric species distribution models.Major Taxa StudiedVirtual plant species.LocationAbruzzo (Italy).Time PeriodTimeless (simulated data).MethodsWe simulated the occurrence of two virtual species with different ecology (generalist vs specialist) and distribution extent. We sampled their occurrence following different sampling strategies: random, stratified, systematic, topographic, uniform within the environmental space (hereafter, uniform) and close to roads. For each sampling design and species, we ran 500 simulations at increasing sampling efforts (total: 42,000 replicates). For each replicate, we fitted a binomial generalised linear model, extracted model coefficients for precipitation and temperature, and compared them with true coefficients from the known species' equation. We evaluated the quality of the estimated response curves by computing bias, variance and root mean squared error (RMSE). Additionally, we (i) assessed the impact of missing covariates on the performance of the sampling approaches and (ii) evaluated the effect of incompletely sampling the environmental space on the uniform approach.ResultsFor the generalist species, we found the lowest RMSE when uniformly sampling the environmental space, while sampling occurrence data close to roads provided the worst performance. For the specialist species, all sampling designs showed comparable outcomes. Excluding important predictors similarly affected all sampling strategies. Sampling limited portions of the environmental space reduced the performance of the uniform approach, regardless of the portion surveyed.Main ConclusionsOur results suggest that a proper estimate of the species response curve can be obtained when the choice of the sampling strategy is guided by the species' ecology. Overall, uniformly sampling the environmental space seems more efficient for species with wide environmental tolerances. The advantage of seeking the most appropriate sampling strategy vanishes when modelling species with narrow realised niches.

Bayesian predictive modeling of multi-source multi-way data

A Bayesian approach to predict a continuous or binary outcome from data that are collected from multiple sources with a multi-way (i.e., multidimensional tensor) structure is described. As a motivating example, molecular data from multiple 'omics sources, each measured over multiple developmental time points, as predictors of early-life iron deficiency (ID) in a rhesus monkey model are considered. The method uses a linear model with a low-rank structure on the coefficients to capture multi-way dependence and model the variance of the coefficients separately across each source to infer their relative contributions. Conjugate priors facilitate an efficient Gibbs sampling algorithm for posterior inference, assuming a continuous outcome with normal errors or a binary outcome with a probit link. Simulations demonstrate that the model performs as expected in terms of misclassification rates and correlation of estimated coefficients with true coefficients, with large gains in performance by incorporating multi-way structure and modest gains when accounting for differing signal sizes across the different sources. Moreover, it provides robust classification of ID monkeys for the motivating application.

Accuracy requirements for the road friction coefficient estimation of a friction-adaptive automatic emergency steer assist (ESA)

The number of traffic accidents resulting in personal injury and property damage is increasingly being reduced by effective advanced driver assistance systems (ADAS). Nevertheless, many traffic accidents still cannot be prevented today because they are due to wet, snow- and ice-covered roads. For this reason, the Institute of Automotive Engineering (IAE) of the Technical University of Braunschweig is investigating the road friction coefficient sensitivity and adaptation of advanced driver assistance systems (ADAS) currently in series production from 2018 to 2021 as part of the ‘Road Condition Cloud’ research project funded by the German Research Foundation (DFG) to increase driving safety, particularly on wet, snow- and ice-covered roads. In this article, the road friction coefficient sensitivity and adaptation of an automatic emergency steer assist is simulatively investigated. This assist overrides the driver to automatically execute an evasive maneuver. The driving maneuver used is a standardized obstacle-avoidance maneuver that is simulatively repeated on a dry, wet, snow- and ice-covered road. The road friction coefficient sensitivity shows that this test is already failed on a wet road because the simulated vehicle does not pass the second lane without errors. Subsequently, a road friction coefficient adaptation of the emergency steer assist is investigated. This adaptation varies the maximum lateral acceleration of the evasive trajectory depending on an estimated value of the road friction coefficient in order not to exceed the maximum adhesion coefficient of the wheels during the evasive maneuver. Ideally, the estimated value matches the true road friction coefficient so that the second lane is passed without errors even on a wet, snow- and ice-covered road. In contrast, an existing difference determines whether the second lane is reached. Finally, the necessary accuracy requirements of the road friction coefficient estimation are determined in an novel estimation error diagram. A road friction coefficient adaptation increases the driving safety of driver advanced assistance systems (ADAS) that are in series production today and future highly automated driving functions (HAF) and is necessary for automated driving because the driver is not present as a fallback level. The described results were presented before in [1].

Comparison on genomic prediction using pedigree BLUP and single step GBLUP through the Hanwoo full-sib family.

When evaluating individuals with the same parent and no phenotype by pedigree best linear unbiased prediction (BLUP), it is difficult to explain carcass grade difference and select individuals because they have the same value in pedigree BLUP (PBLUP). However, single step GBLUP (ssGBLUP), which can estimate the breeding value suitable for the individual by adding genotype, is more accurate than the existing method. The breeding value and accuracy were estimated with pedigree BLUP and ssGBLUP using pedigree and genotype of 408 Hanwoo cattle from 16 families with the same parent among siblings produced by fertilized egg transplantation. A total of 14,225 Hanwoo cattle with pedigree, genotype and phenotype were used as the reference population. PBLUP obtained estimated breeding value (EBV) using the pedigree of the test and reference populations, and ssGBLUP obtained genomic EBV (GEBV) after constructing and H-matrix by integrating the pedigree and genotype of the test and reference populations. For all traits, the accuracy of GEBV using ssGBLUP is 0.18 to 0.20 higher than the accuracy of EBV obtained with PBLUP. Comparison of EBV and GEBV of individuals without phenotype, since the value of EBV is estimated based on expected values of alleles passed down from common ancestors. It does not take Mendelian sampling into consideration, so the EBV of all individuals within the same family is estimated to be the same value. However, GEBV makes estimating true kinship coefficient based on different genotypes of individuals possible, so GEBV that corresponds to each individual is estimated rather than a uniform GEBV for each individual. Since Hanwoo cows bred through embryo transfer have a high possibility of having the same parent, if ssGBLUP after adding genotype is used, estimating true kinship coefficient corresponding to each individual becomes possible, allowing for more accurate estimation of breeding value.

Cryptosporidiosis threat under climate change in China: prediction and validation of habitat suitability and outbreak risk for human-derived Cryptosporidium based on ecological niche models

BackgroundCryptosporidiosis is a zoonotic intestinal infectious disease caused by Cryptosporidium spp., and its transmission is highly influenced by climate factors. In the present study, the potential spatial distribution of Cryptosporidium in China was predicted based on ecological niche models for cryptosporidiosis epidemic risk warning and prevention and control.MethodsThe applicability of existing Cryptosporidium presence points in ENM analysis was investigated based on data from monitoring sites in 2011–2019. Cryptosporidium occurrence data for China and neighboring countries were extracted and used to construct the ENMs, namely Maxent, Bioclim, Domain, and Garp. Models were evaluated based on Receiver Operating Characteristic curve, Kappa, and True Skill Statistic coefficients. The best model was constructed using Cryptosporidium data and climate variables during 1986‒2010, and used to analyze the effects of climate factors on Cryptosporidium distribution. The climate variables for the period 2011‒2100 were projected to the simulation results to predict the ecological adaptability and potential distribution of Cryptosporidium in future in China.ResultsThe Maxent model (AUC = 0.95, maximum Kappa = 0.91, maximum TSS = 1.00) fit better than the other three models and was thus considered the best ENM for predicting Cryptosporidium habitat suitability. The major suitable habitats for human-derived Cryptosporidium in China were located in some high-population density areas, especially in the middle and lower reaches of the Yangtze River, the lower reaches of the Yellow River, and the Huai and the Pearl River Basins (cloglog value of habitat suitability > 0.9). Under future climate change, non-suitable habitats for Cryptosporidium will shrink, while highly suitable habitats will expand significantly (χ2 = 76.641, P < 0.01; χ2 = 86.836, P < 0.01), and the main changes will likely be concentrated in the northeastern, southwestern, and northwestern regions.ConclusionsThe Maxent model is applicable in prediction of Cryptosporidium habitat suitability and can achieve excellent simulation results. These results suggest a current high risk of transmission and significant pressure for cryptosporidiosis prevention and control in China. Against a future climate change background, Cryptosporidium may gain more suitable habitats within China. Constructing a national surveillance network could facilitate further elucidation of the epidemiological trends and transmission patterns of cryptosporidiosis, and mitigate the associated epidemic and outbreak risks.Graphical

880 Electrophysiological Patterns of Glioma-Induced Neuronal Network Remodeling are a General Property of Brain Tumors Regardless of Subtype

INTRODUCTION: Recent evidence indicates that diffuse gliomas engage with neurons at the single-unit and circuit level through differing mechanisms. Certain malignant gliomas form glioma-neuron excitatory glutamatergic synapses and modulate neuron-neuron synapses through activity-dependent paracrine signaling, while others establish glioma-glioma connections via tumor microtubes. It is therefore possible that diffuse gliomas remodel neuronal circuits in a defined and predictable manner and demonstrate distinct electrophysiological profiles with prognostic and therapeutic significance. METHODS: Following spatial-temporal registration, an elastic net logistic regression classifier was used to distinguish between power spectra arising from glioma-remodeled cortex and within-subject control conditions (N = 140). Model significance was determined non-parametrically by re-training each model 1,000 times with randomly permuted class labels and testing the true phi coefficient against the null distribution. RESULTS: In the discovery dataset, glioma infiltration was accurately classified based on tumor intrinsic neuronal activity (p &lt; 0.05) in 127 patients (90.7%). Thirty electrophysiological features were identified, which revealed increased power in the delta range (1-4 Hz) and decreased power in the beta range (12-20 Hz) as a unique signature of glioma remodeling (p &lt; 0.05) which was preserved in the validation dataset as well as across WHO 2021 diffuse glioma subtypes. To identify gene expression programs and signaling mechanisms that may contribute to glioma-induced remodeling but are potentially not identified in the current clinical classification scheme, targeted, next generation sequencing was performed and used as covariates, which again demonstrated the significance of the delta-beta spectral features. CONCLUSIONS: These data support converging mechanisms of glioma-induced neuronal network remodeling across tumor subtypes, setting the stage for novel therapies such as neuromodulation.

Multi-functional electroactive tetrafluoroethylene-hexafluoropropylene-vinylidene fluoride (THV)/magnesium diboride (MgB2) composite reactive material

Fluoropolymers have weak electroactive properties but are often paired with other materials (inclusions) to form a composite with enhanced electroactive properties and when those inclusions are a metal or metalloid fuel (e.g., Al, Si, B) they can then function as electroactive reactive materials. There is strong interest in understanding the electromechanical (piezo, flexo, dielectric) properties toward the development of multifunctional energetics. Specifically, in this effort, we report the influence of magnesium diboride (MgB2) on the electroactive and combustion properties in a composite system with the fluoropolymer tetrafluoroethylene-hexafluoropropylene-vinylidene fluoride (THV). The dielectric, flexoelectric, piezoelectric, burning rate, and heat of reaction are investigated for composites with 50–70 wt. % MgB2 loading. It is observed that with an increase in solids loading that the dielectric, flexoelectric, and burning rate increase. For example, at 70 wt. % MgB2, the measured dielectric constant was 298 ± 10, and the flexoelectric coefficient was 16.6 ± 1.2 nC/m. Unexpectedly, a true piezoelectric coefficient (d33) was measured between 11.4 ± 0.2 and 13.0 ± 0.3 pC/N at these solids loadings. Due to the mechanical properties of these composite systems, an apparent piezoelectric coefficient of 108 pC/N was calculated indicating that these reactive materials are highly electroactive. The multifunctional properties are demonstrated by applying the force of the human body (via jumping) to a THV/MgB2 (50 wt. %) film. A peak voltage of 0.75 V was observed. This work indicates that a variety of reactive materials may offer multifunctional capabilities.

True ileal digestibility of phosphorus from dicalcium phosphate in diets for broilers

The objective of this study was to determine the true ileal digestibility coefficient of phosphorus (TIDCP) of three dicalcium phosphates of different origins in diets for broilers using the regression method. The experimental design was completely randomized in a 3 × 2 + 1 factorial scheme with three dicalcium phosphates (DCP1, DCP2 and DCP3), two inclusion values (0.75 and 1.5 g/kg of P), and a basal diet. The seven diets were formulated based on corn and soybean meal: one (basal) diet lacked dicalcium phosphate, and six diets contained a concentration gradient of the test ingredients. Each test ingredient had two inclusion values of 0.75 and 1.5 g/kg of P. Titanium dioxide was added to the experimental diets at 5 g/kg as an indigestible indicator. A total of 252 Cobb 500 male broilers, 19 days old (average weight ± SD, 806 ± 29 g) were housed in metabolic cages and assigned to the seven test diets. On the 22nd day of life, all birds were euthanized, and the ileal contents were collected. The apparent digestibility of the diets was influenced by dicalcium phosphate source (P < 0.05), with greater P digestibility birds that received diets with the inclusion of DCP2. P digestibility was unaffected (P > 0.05) by increasing dietary concentrations of P. The TIDCP of DCP1, DCP2 and DCP3 was determined, by the regression method, to be 0.3639, 0.4897 and 0.3553, respectively. The endogenous P losses estimated by the regression method for DCP1, DCP2, and DCP3 were 0.66, 0.27 and 0.76 g/kg DM, respectively. In despite of differences in diet digestibility, there were no differences between TIDCP values (P > 0.05), as determined by covariance analysis. The use of a medium value of P digestibility for different sources of dicalcium phosphates can lead to errors or the use of large safety margins in the formulation of broiler diet.

Изучение реакции ионов Cu(II) с N-(бензоил)-N'-(фенилсульфонил)гидразином в аммиачных средах спектрофотометрическим методом

The reaction of complexation of N-(benzoyl)-N'-(phenylsulfonyl)hydrazine with Cu(II) ions in an ammonia medium has been studied. The absorption spectra of the reagent and its complex with Cu(II) ions have been recorded and analyzed. The optimal conditions for the formation of the complex compound were found: the pH range of complex formation; optimal time for color development; amount of photometric reagent. Under optimal conditions for the complex formation of BPSH with Cu(II) ions, a calibration graph was constructed. The Bouguer-Lambert-Beer law is fulfilled in the range from 0,5 to 1,75 mg Cu(II) in 25 ml of solution. The true (19389) and apparent (4708) molar light absorption coefficients were calculated. The methods of saturation, equilibrium shift, curve intersection, isomolar series, and conductometric titration were used to determine the molar ratios in a solution of a complex compound – [Сu(II)]:[BFSH] = 1:1 and 1:2. According to the Babko method, the conditional stability constant of the complex was calculated – 2,45∙1013. The convergence and correctness of the spectrophotometric technique were determined by the method of mathematical statistics.

Optimal designs for the development of personalized treatment rules

AbstractWe study the design of multi‐armed parallel group clinical trials to estimate personalized treatment rules that identify the best treatment for a given patient with given covariates. Assuming that the outcomes in each treatment arm are given by a homoscedastic linear model, with possibly different variances between treatment arms, and that the trial subjects form a random sample from an unselected overall population, we optimize the (possibly randomized) treatment allocation allowing the allocation rates to depend on the covariates. We find that, for the case of two treatments, the approximately optimal allocation rule does not depend on the value of the covariates but only on the variances of the responses. In contrast, for the case of three treatments or more, the optimal treatment allocation does depend on the values of the covariates as well as the true regression coefficients. The methods are illustrated with a recently published dietary clinical trial.

CNSC-05. ELECTROPHYSIOLOGICAL PATTERNS OF GLIOMA-INDUCED NEURONAL NETWORK REMODELING ARE CONSERVED ACROSS TUMOR SUBTYPE

Abstract Recent evidence indicates that diffuse gliomas engage with neurons at the single-unit and circuit level through differing mechanisms. Certain malignant gliomas form glioma-neuron excitatory glutamatergic synapses and modulate neuron-neuron synapses through activity-dependent paracrine signaling, while others establish glioma-glioma connections via tumor microtubes. It is therefore possible that diffuse gliomas remodel neuronal circuits in a defined and predictable manner and demonstrate distinct electrophysiological profiles with prognostic and therapeutic significance. Here we apply machine learning principles in 140 patients across glioma subtypes to uncover unique electrophysiological features non-invasively via magnetoencephalography (discovery dataset) followed by feature validation using subdural electrocorticography (validation dataset). Following spatial-temporal registration, we fit an elastic net logistic regression classifier to distinguish between power spectra arising from glioma-remodeled cortex and within-subject control conditions. Model significance was determined non-parametrically by re-training each model 1,000 times with randomly permuted class labels and testing the true phi coefficient against the null distribution. In the discovery dataset, we were able to classify glioma infiltration based on tumor intrinsic neuronal activity (p &amp;lt; 0.05) in 127 patients (90.7%). We identified 30 electrophysiological features which revealed increased power in the delta range (1-4 Hz) and decreased power in the beta range (12-20 Hz) as a unique signature of glioma remodeling (p &amp;lt; 0.05) which was preserved in the validation dataset as well as across WHO 2021 diffuse glioma subtypes. In order to identify gene expression programs and signaling mechanisms that may contribute to glioma-induced remodeling but are potentially not identified in the current clinical classification scheme, we assessed targeted, next generation sequencing and DNA mutations as covariates, which again demonstrated the significance of the delta-beta spectral features. These data support converging mechanisms of glioma-induced neuronal network remodeling across tumor subtypes, setting the stage for novel therapies such as neuromodulation.

Design of a simple radio frequency circuit for implementing the open-ended coaxial probe method for permittivity measurement.

The development of electromagnetic (EM)-based therapeutic and diagnostic tools, as well as safety assessment of EM interactions with the human body, requires adequate measurement of the complex permittivity of different biological tissues. Such measurement techniques must be low-cost, readily available, and easy to implement. In this study, a simple circuit with basic radio frequency electronics was used to implement the open-ended coaxial probe method for permittivity measurement, as opposed to the widely used vector network analyzers. The non-ideal behavior of the circuit due to spurious reflections and ohmic losses was accounted for by a scattering matrix (SM) that relates the measured reflection coefficient to the true reflection coefficient at the probe tip. Parameters of SM were obtained using three calibration standards, and the circuit was used to measure the complex permittivity of a standard, tissue-equivalent, American Society of Testing Materials (ASTM) polymer gel. A more intuitive approach to circuit analysis is also introduced. For both methods, the dielectric constant and electrical conductivity of the gel were found to agree with the recommended uncertainties of the ASTM standard and validate the utility of the circuit at the test frequency.

Simultaneous confidence bands and global inferences for extended partially linear single-index models

Substantial efforts have been made for the popular semiparametric single-index models in the last two decades. Extended partially linear single-index models as generalized single-index semiparametric models effectively reduce the problem of model misspecification by allowing the data to automatically choose the principal linear component and the principal nonlinear component. In this paper, we construct an oracle-efficient simultaneous confidence band as a global inference tool for the extended partially linear single-index models. Specifically, we apply a LASSO penalized local linear smoothing method to estimate the coefficient parameters and conduct variable selection to avoid model overfitting caused by the two appearances of covariates. It is shown that the local linear estimator for the nonparametric link function by employing the penalized coefficient estimates is oracle-efficient in the sense that it is uniformly as efficient as the ideal one obtained by utilizing the true coefficient parameters. Then by applying the oracle efficiency and the extreme value distribution theory of the local linear regression, an asymptotically accurate simultaneous confidence band for the nonparametric link function in the extended partially linear single-index models is established. Simulation experiments with commonly encountered sample sizes corroborate our theoretical findings. The proposed method is applied to analyze two engineering datasets: motor trend car road tests dataset and concrete slump tests dataset.

Ridge Regression and the Elastic Net: How Do They Do as Finders of True Regressors and Their Coefficients?

For the linear model Y=Xb+error, where the number of regressors (p) exceeds the number of observations (n), the Elastic Net (EN) was proposed, in 2005, to estimate b. The EN uses both the Lasso, proposed in 1996, and ordinary Ridge Regression (RR), proposed in 1970, to estimate b. However, when p&gt;n, using only RR to estimate b has not been considered in the literature thus far. Because RR is based on the least-squares framework, only using RR to estimate b is computationally much simpler than using the EN. We propose a generalized ridge regression (GRR) algorithm, a superior alternative to the EN, for estimating b as follows: partition X from left to right so that every partition, but the last one, has 3 observations per regressor; for each partition, we estimate Y with the regressors in that partition using ordinary RR; retain the regressors with statistically significant t-ratios and the corresponding RR tuning parameter k, by partition; use the retained regressors and k values to re-estimate Y by GRR across all partitions, which yields b. Algorithmic efficacy is compared using 4 metrics by simulation, because the algorithm is mathematically intractable. Three metrics, with their probabilities of RR’s superiority over EN in parentheses, are: the proportion of true regressors discovered (99%); the squared distance, from the true coefficients, of the significant coefficients (86%); and the squared distance, from the true coefficients, of estimated coefficients that are both significant and true (74%). The fourth metric is the probability that none of the regressors discovered are true, which for RR and EN is 4% and 25%, respectively. This indicates the additional advantage RR has over the EN in terms of discovering causal regressors.

Correlation uncertainty, limited participation, and flight to quality

This study extends the multi-asset model of Huang et al. (2017), who examine only two types of investors, by adding a new investor type with partial information on the correlation coefficient and re-explores the limited participation phenomenon under correlation ambiguity. We investigate whether asset allocations depend on incomplete information under market equilibrium—specifically, whether investors with less information might hold greater equilibrium positions than investors with more information. We find that, as the true correlation coefficient (and the maximum correlation coefficient for ambiguity-averse investors) increases and asset quality increases, investors with less information escape from low- to high-quality assets, thus exhibiting a flight-to-quality trading pattern in equilibrium.

Influence of pulse-jet cleaning pressure on performance of compact dust collector with pleated filter operated in clean-on-time mode

• Accumulation structure of residual dust particles was studied for a filter medium. • Different pulse-jet pressures were considered, and a simple model was used. • Filter performance was studied by analyzing the dust accumulation structure. • The effective ratio of filtration area increased with pulse-jet pressure. • The dust penetration increased with pulse-jet pressure. To operate a bag filter continuously, pulse-jet cleaning of dust particles from the filter medium is commonly required, and the pulse-jet pressure significantly affects the filter performance. In this study, the accumulation structure of residual dust particles inside and on the surface of a filter medium at different pulse-jet pressures was investigated by constructing a simple model, and the influence of the dust structure on the filter performance was clarified. Using a simple model, we determined the effective ratio of filtration area β , which represents the ratio of the filterable area to the total filtration area, the true resistance coefficient due to the primary dust layer ζ p ’ thinly deposited on the filter surface, and the true resistance coefficient inside the filter media itself ζ f ’ . The effective ratio of filtration area β decreased with operation time for all pulse-jet pressures; however, it maintained a high value when the pulse-jet pressure was high. The validity of β analyzed by the model was verified using two different methods, and the results showed good agreement, indicating that the model is effective in identifying real conditions. The true resistance coefficient due to the primary dust layer ζ p ’ decreased as the pulse-jet pressure increased; however, the true resistance coefficient inside the filter media itself ζ f ’ was the highest at 0.5 MPa. In addition, the dust collection efficiency was different at each pulse-jet pressure, which was considered to be caused by the difference in the dust particle accumulation structure.

The asymptotic distribution of the MLE in high-dimensional logistic models: Arbitrary covariance

We study the distribution of the maximum likelihood estimate (MLE) in high-dimensional logistic models, extending the recent results from Sur (2019) to the case where the Gaussian covariates may have an arbitrary covariance structure. We prove that in the limit of large problems holding the ratio between the number $p$ of covariates and the sample size $n$ constant, every finite list of MLE coordinates follows a multivariate normal distribution. Concretely, the $j$th coordinate $\hat {\beta}_j$ of the MLE is asymptotically normally distributed with mean $\alpha_\star \beta_j$ and standard deviation $\sigma_\star/\tau_j$; here, $\beta_j$ is the value of the true regression coefficient, and $\tau_j$ the standard deviation of the $j$th predictor conditional on all the others. The numerical parameters $\alpha_\star > 1$ and $\sigma_\star$ only depend upon the problem dimensionality $p/n$ and the overall signal strength, and can be accurately estimated. Our results imply that the MLE's magnitude is biased upwards and that the MLE's standard deviation is greater than that predicted by classical theory. We present a series of experiments on simulated and real data showing excellent agreement with the theory.

Asset allocation, limited participation and flight‐to‐quality under ambiguity of correlation

AbstractThis paper investigates asset allocation decisions made by three types of traders depending upon incomplete information in market equilibrium. Limited participation phenomenon is observed in the equilibrium. Moreover, we show that traders with more information might not hold more risky assets than others who have less information. Less‐informed traders trade‐off between a diversification effect induced by risk‐averse attitude and a “flight‐to‐quality” effect by their aversion towards correlation ambiguity. In equilibrium, the magnitudes between equilibrium positions of different traders are affected by the true correlation coefficient, the upper‐bound correlation conceived by naive traders and the quality of risky assets simultaneously. In some scenarios, we observe that naive and uninformed traders “escape” from low‐quality assets to high‐quality ones.