Estimation Step Research Articles

Array auto-calibration using a generalized least-squares method

This paper introduces a new class of auto-calibration algorithms, based on a generalized least-squares (GLS) cost function. This class supports both gain-phase calibration and mutual coupling calibration and it is independent of array shape. The GLS cost function is minimized cyclically with respect to the direction of arrivals (DOA) and calibration parameters. These two minimization steps are repeated until convergence. In the DOA estimation step, minimization leads to the SPICE (SParse Iterative Covariance-based Estimation) DOA estimation algorithm. In fact, the innovation of this paper mainly lies in the calibration step. In the DOA estimation step, a generalized form of the SPICE algorithm should be used in the coherent sources case. Consequently, the proposed auto-calibration algorithm is able to support coherent source cases. Moreover, the proposed algorithm does not need multi-dimensional search. The necessary condition for the existence of a solution is studied as well. Using simulations, the performance of the proposed algorithm is compared with several other auto-calibration algorithms. Simulation results show that the proposed algorithm is more robust than the other algorithms and usually has better performance.

Jackknife resample method for precision estimation of weighted total least squares

Few studies have been conducted on the precision estimation of weighted total least squares (WTLS) by using the approximate function probability distribution method. And the existing Monte Carlo method needs to simulate a lot, the amount of calculation is large and the results obtained are uncertain. In order to further improve the total least squares precision estimation theory, this paper introduces the Jackknife method into Geomatics data processing. Combining the Jackknife method and WTLS method, the delete-1 Jackknife method and delete-d Jackknife method are proposed. The biases and standard deviations or covariance of parameter estimations are calculated by these proposed methods. And the specific steps of the precision estimation of these two methods are given. Applying these methods to the linear regression model and the coordinate transformation model, and comparing with the approximate function method and the Monte Carlo method, we can see that the Jackknife methods for precision estimation can obtain more stable and reasonable precision results and are very adaptive. In order to get more reasonable precision results, the Jackknife method does spend much more time over total least squares when the amount of the observed data is large. But compared with Monte Carlo method, it can reduce the amount of calculation and improve the computational efficiency. The method in this paper could provide an idea for further study on the precision estimation for total least squares.

Wasserstein covariance for multiple random densities

A common feature of methods for analyzing samples of probability density functions is that they respect the geometry inherent to the space of densities. Once a metric is specified for this space, the Frechet mean is typically used to quantify and visualize the average density from the sample. For one-dimensional densities, the Wasserstein metric is popular due to its theoretical appeal and interpretive value as an optimal transport metric, leading to the Wasserstein-Frechet mean or barycenter as the mean density. We extend the existing methodology for samples of densities in two key directions. First, motivated by applications in neuroimaging, we consider dependent density data, where a $p$-vector of univariate random densities is observed for each sampling unit. Second, we introduce a Wasserstein covariance measure and propose intuitively appealing estimators for both fixed and diverging $p$, where the latter corresponds to continuously-indexed densities. We also give theory demonstrating consistency and asymptotic normality, while accounting for errors introduced in the unavoidable preparatory density estimation step. The utility of the Wasserstein covariance matrix is demonstrated through applications to functional connectivity in the brain using functional magnetic resonance imaging data and to the secular evolution of mortality for various countries.

Query-Based Sensors Selection for Collaborative Wireless Sensor Networks With Stochastic Energy Harvesting

We develop a new statistical decision making framework to select the optimal subset of sensors to activate, while meeting various quality of service criteria specified by users’ queries. The sensor nodes are powered solely by energy harvested from the environment and should be activated in an efficient and economical manner based on the available battery energy, which may not be directly observed by the decision maker. Our decision making framework consists of two aspects: the first is the estimation of the current available battery levels of each of the sensors and the second is a sensor selection policy. The energy estimation step is based on the Cumulative Energy Harvesting Process which is carried out over the collaborative wireless sensor networks. The sensor selection policy is based on the estimated battery levels and uses the Cross-Entropy method which efficiently solves the resulting combinatorial problem to select the sensor set which maximizes the utility function. We demonstrate and provide insights into the robustness and effectiveness of our framework under different operational conditions.

Sparsity Optimization Method for Slow-Moving Landslides Detection in Satellite Image Time-Series

This paper presents a new method based on recent optimization technique to detect slow-moving landslides (<;150m/year) in time series of displacement field generated by satellite images. Sparse optimization is applied simultaneously on the 3-D data set in space as well as in time. The proposed method takes into account the distinctive signal physical properties in space and time, by enforcing a sparse representation by blocks in space, but a continuing and monotonous representation in time of the landslides. As a result, we show that a mixed ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> ,2-norm is the most suitable norm for this detection problem, compared to pure ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm or ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm. Moreover, an outlier estimation step is included that sets apart the Gaussian noise from locally sparse processing errors in the data. The performance of this approach is tested by applying it both on synthetic data and on a time series of displacements fields over 16 dates in the Colca Valley, Peru. This detection presents commission and omission errors for landslides of 29% and 14%, respectively, using a medium resolution (10 m) data set of optical satellite images. It detects all important landslides, already known from field investigations. Moreover, it also points out other smaller or unknown landslides, increasing the existing slow-moving landslide inventory by +50%.

Nonseparability Without Monotonicity: The Couterfactual Distribution Estimator for Causal Inference

Nonparametric identification strategy is employed to capture causal relationships without imposing any variant of monotonicity existing in the nonseparable nonlinear error model literature. This is important as when monotonicity is applied to the instrumental variables it limits their availability and when applied to the unobservables it can hardly be justified in the non-scalar case. Moreover, in cases where monotonicity is not satisfied the monotonicity-based estimators might be severely biased as shown in comparative Monte Carlo simulation. The key idea in the proposed identification and estimation strategy is to uncover the counterfactual distribution of the dependent variable, which is not directly observed in the data. We offer a two-step M-Estimator based on a resolution-dependent reproducing symmetric kernel density estimator rather than on the bandwidth-dependent classical kernel and thus, less sensitive to bandwidth choice. Additionally, the average marginal effect of the endogenous covariate on the outcome variable is identified directly from the noisy data which precludes the need to employ additional estimation steps thereby avoiding potential error accumulation. Asymptotic properties of the counterfactual M-Estimator are established.

Improved functional portmanteau tests

ABSTRACTFunctional time series is a popular method of forecasting in functional data analysis. The Box-Jenkins methodology for model building, with the aim of forecasting, includes three iterative steps of model identification, parameter estimation and diagnostic checking. Portmanteau tests are one of the most popular diagnostic checking tools. In particular, they are applied to find if the residuals of the fitted model are white noise. Gabrys and Kokoszka [Portmanteau test of independence for functional observations. J Am Stat Assoc. 2007;102(480):1338–1348.] proposed a portmanteau test of independence for functional observation based on Box and Pierce's statistic. Their statistic is too sensitive to the lag value, specially when the sample size is small. Here, two modifications of Gabrys and Kokoszka statistic are presented, which have superior properties in small samples. The efficiency of the modified statistics is demonstrated through a simulation study.

Shape geodesics for robust sign language recognition

In this study, a novel method of pattern recognition for static gesture recognition is proposed. To deal with this application, the authors should tackle the problem of low variability among shape classes in the gestures databases. The authors' method is based on robust registration and shape geodesics in shape space with a preliminary step of pose estimation accelerating the processing time. The different gestures are considered individual points in a non-linear shape space. Similarity between any two considered shapes can be measured by a distance metric on the shape space. For shape comparison, three distances with and without robust norm are proposed and evaluated for the target application. To cope with low shape variability among classes, the best scheme is to use robust norm in shape matching but not in shape comparison. Experiments are conducted on reference databases used in the literature to evaluate static gesture recognition. These experiments show the outperformance of the proposed scheme compared to state-of-the art methods.

Spatial Scale Consideration for Estimating All-Sky Surface Shortwave Radiation With a Modified 1-D Radiative Transfer Model

The spatial scale variability of surface shortwave radiation (SSR) budget is dominated by clouds. Therefore, a prerequisite step for accurate estimation of the instantaneous SSR components under all-sky conditions, especially partly cloudy conditions, is to determine a proper spatial scale. In this study, a modified one-dimensional (1-D) radiative transfer (RT) model is developed to study the effect of cloud cover on SSR field, in which cloud fraction (CF) is introduced to improve the classic 1-D plane-parallel RT equations and a global sensitivity analysis (GSA) is performed to quantitatively understand the effect of CF on SSR under both optically thick and optically thin cloudy conditions. An artificial neural network approach is then employed to generate multiple SSR components based on extensive RT simulations using the modified 1-D RT model and the GSA results. The optimal spatial scale is quantitatively determined through eight cloudy scenarios over the Tibetan Plateau, with different amounts and spatial distribution patterns of clouds. The GSA results show that the top three vital parameters for the modified 1-D RT model are solar zenith angle, CF, and land surface albedo. The optimal spatial scale for applying the modified 1-D RT model is about 20 km, which is surprisingly consistent with some theoretically and technically complicated simulation studies. The finding of the current study adds new evidence to the growing body of knowledge about the spatial scale consideration for estimating all-sky instantaneous SSR with 1-D RT theory.

Digital Module 04: Diagnostic Measurement: Modeling Checklists for Practitioners https://ncme.elevate.commpartners.com

AbstractIn this ITEMS module, we provide a didactic overview of the specification, estimation, evaluation, and interpretation steps for diagnostic measurement/classification models (DCMs), which are a promising psychometric modeling approach. These models can provide detailed skill‐ or attribute‐specific feedback to respondents along multiple latent dimensions and hold theoretical and practical appeal for a variety of fields. We use a current unified modeling framework—the log‐linear cognitive diagnosis model (LCDM)—as well as a series of quality‐control checklists for data analysts and scientific users to review the foundational concepts, practical steps, and interpretational principles for these models. We demonstrate how the models and checklists can be applied in real‐life data‐analysis contexts. A library of macros and supporting files for Excel, SAS, and Mplus are provided along with video tutorials for key practices.

GMM estimation of simultaneous spatial panel data dynamic models with high order models approach

This paper shows the process of changing Simultaneous Spatial panel data dynamic Equations (SSPDD) into a high order model, which is then, parameterized in the high order model using the General method of Moment (GMM). The estimation steps start by forming the reduced form equation, and then form the instrumental Variable (IV) vector which is used to form the moment matrix gnt(θ). This moment matrix is used to calculate the estimated value of the model parameters. In this paper, the results of parameter estimation have not been tested with data or simulations so that it is recommended to do further testing using simulation data.

HOOFR SLAM System: An Embedded Vision SLAM Algorithm and Its Hardware-Software Mapping-Based Intelligent Vehicles Applications

This paper deals simultaneously with the trajectory estimation and map reconstruction by means of a stereo-calibrated vision system evolving in a large-scale unknown environment. This problem is widely known as Visual SLAM. Our proposal optimizes the execution time of the VSLAM framework while preserving its localization accuracy. The contributions of this paper are structured as follows. First, a novel VSLAM approach based on a “Weighted Mean” of multiple neighbor poses is detailed and is denoted as HOOFR SLAM. This approach provides a localization estimate after computing the camera poses (6-DOF rigid transformation) from the current image frame to previous neighbor frames. Taking advantage of the camera motion, we conjointly incorporate two types of stereo modes: “Static Stereo” mode (SS) through the fixed-baseline of left-right cameras setup along with the “Temporal Multi-view Stereo” mode (TMS). Moreover, instead of computing beforehand the disparity of SS mode for all key-points set, the disparity map in scale estimation step is limited to the inliers of the TMS mode so as to reduce the computational cost. This strategy is suitable to be parallelized on a multiprocessor architecture and exhibits a competitive performance with the other state-of-the-art strategies in many real datasets. Second, we report a hardware-software mapping of the proposed VSLAM approach. To this end, a heterogeneous CPU-GPU architecture-based vision system is considered. Third, a thorough and extensive experimental evaluation of our algorithm implemented on an automotive architecture (the NVIDIA Tegra TX1 system) is studied and analyzed. We report hence the localization and timing results through experiments on five well-known public stereo SLAM datasets: KITTI, Malaga, Oxford, MRT, and St_Lucia datasets.

Estimation with Flexible Probabilities: Identifying Rand Hedges, Finding Diversifiers, Enhancing Style Analysis

At its core, portfolio and risk management is about gathering and processing market-related data in order to make effective investment decisions. To this end, risk and return statistics are estimated from relevant financial data and used as inputs within the investment process. It is this estimation step which is ultimately key in transforming raw financial data into useful investment information. Therefore, having a flexible and robust estimation process is of crucial importance to all market participants. In this work we make use of the Flexible Probabilities framework and show how to incorporate different forms of time-conditioning and market state-conditioning into any estimation procedure. This framework allows one to calculate conditional risk and return statistics in a simple manner for a wide range of portfolio and risk management applications. We then make use of this framework in three applications which we believe to be directly relevant to South African market participants. Firstly, we address the long-standing issue of identifying rand-hedge stocks and quantifying the extent of their hedging ability, specifically in times of rand weakness and strength. Secondly, we examine whether the ‘myth of diversification’ – that diversification disappears when one needs it most – holds true for South African markets. Finally, we extend the original returns-based style analysis framework to allow for time- and state-conditioned weights and estimate the style mix of the South African equity market across time and market regime.

Dynamic Load Balancing Techniques for Particulate Flow Simulations

Parallel multiphysics simulations often suffer from load imbalances originating from the applied coupling of algorithms with spatially and temporally varying workloads. It is, thus, desirable to minimize these imbalances to reduce the time to solution and to better utilize the available hardware resources. Taking particulate flows as an illustrating example application, we present and evaluate load balancing techniques that tackle this challenging task. This involves a load estimation step in which the currently generated workload is predicted. We describe in detail how such a workload estimator can be developed. In a second step, load distribution strategies like space-filling curves or graph partitioning are applied to dynamically distribute the load among the available processes. To compare and analyze their performance, we employ these techniques to a benchmark scenario and observe a reduction of the load imbalances by almost a factor of four. This results in a decrease of the overall runtime by 14% for space-filling curves.

Progressive Learning for Person Re-Identification with One Example.

In this paper, we focus on the one-example person re-identification (re-ID) task, where each identity has only one labeled example along with many unlabeled examples. We propose a progressive framework which gradually exploits the unlabeled data for person re-ID. In this framework, we iteratively (1) update the Convolutional Neural Network (CNN) model and (2) estimate pseudo labels for the unlabeled data. We split the training data into three parts, i.e., labeled data, pseudo-labeled data, and indexlabeled data. Initially, the re-ID model is trained using the labeled data. For the subsequent model training, we update the CNN model by the joint training on the three data parts. The proposed joint training method can optimize the model by both the data with labels (or pseudo labels) and the data without any reliable labels. For the label estimation step, instead of using a static sampling strategy, we propose a progressive sampling strategy to increase the number of the selected pseudo-labeled candidates step by step. We select a few candidates with most reliable pseudo labels from unlabeled examples as the pseudo-labeled data, and keep the rest as index-labeled data by assigning them with the data indexes. During iterations, the index-labeled data are dynamically transferred to pseudo-labeled data. Notably, the rank-1 accuracy of our method outperforms the state-of-the-art method by 21.6 points (absolute, i.e., 62.8% vs. 41.2%) on MARS, and 16.6 points on DukeMTMC-VideoReID. Extended to the few-example setting, our approach with only 20% labeled data surprisingly achieves comparable performance to the supervised state-of-the-art method with 100% labeled data.

A Novel Multi-Hop Algorithm for Wireless Network with Unevenly Distributed Nodes

Node location estimation is not only the promise of the wireless network for target recognition, monitoring, tracking and many other applications, but also one of the hot topics in wireless network research. In this paper, the localization algorithm for wireless network with unevenly distributed nodes is discussed, and a novel multi-hop localization algorithm based on Elastic Net is proposed. The proposed approach is formulated as a regression problem, which is solved by Elastic Net. Unlike other previous localization approaches, the proposed approach overcomes the shortcomings of traditional approaches assume that nodes are distributed in regular areas without holes or obstacles, therefore has a strong adaptability to the complex deployment environment. The proposed approach consists of three steps: the data collection step, mapping model building step, and location estimation step. In the data collection step, training information among anchor nodes of the given network is collected. In mapping model building step, the mapping model among the hop-counts and the Euclidean distances between anchor nodes is constructed using Elastic Net. In location estimation step, each normal node finds its exact location in a distributed manner. Realistic scenario experiments and simulation experiments do exhibit the excellent and robust location estimation performance.

Task activations produce spurious but systematic inflation of task functional connectivity estimates

Most neuroscientific studies have focused on task-evoked activations (activity amplitudes at specific brain locations), providing limited insight into the functional relationships between separate brain locations. Task-state functional connectivity (FC) – statistical association between brain activity time series during task performance – moves beyond task-evoked activations by quantifying functional interactions during tasks. However, many task-state FC studies do not remove the first-order effect of task-evoked activations prior to estimating task-state FC. It has been argued that this results in the ambiguous inference "likely active or interacting during the task", rather than the intended inference "likely interacting during the task". Utilizing a neural mass computational model, we verified that task-evoked activations substantially and inappropriately inflate task-state FC estimates, especially in functional MRI (fMRI) data. Various methods attempting to address this problem have been developed, yet the efficacies of these approaches have not been systematically assessed. We found that most standard approaches for fitting and removing mean task-evoked activations were unable to correct these inflated correlations. In contrast, methods that flexibly fit mean task-evoked response shapes effectively corrected the inflated correlations without reducing effects of interest. Results with empirical fMRI data confirmed the model's predictions, revealing activation-induced task-state FC inflation for both Pearson correlation and psychophysiological interaction (PPI) approaches. These results demonstrate that removal of mean task-evoked activations using an approach that flexibly models task-evoked response shape is an important preprocessing step for valid estimation of task-state FC.

Using huge amounts of road sensor data for official statistics

On the Dutch road network, about 60,000 road sensors are located of which 20,000 sensors are on the Dutch highways. Both vehicle counts and average speed are collected each minute and stored in the National Traffic Daffic statistics several methodological challenges needed to be solved. The first was developing a method to check and improve the data quality as quite some sensors lacked data for many minutes during the day. A cleaning and estimation step was implemented that enabled a precise and accurate estimate of the number of vehicles actually passing the sensors for each minute. The second challenge was monitoring the stream of incoming and outgoing data and controlling this fully automatic statistical process. This required defining quality indicators on the raw and processed sensor data. The fourth challenge was determining calibration weights based on the geographic locations of the road sensors on the roads. This was needed because road sensors are not uniformly distributed over the road network. As the number of active sensors fluctuates over time, the weights need to be determined periodically. As a result of these steps accurate numbers could be produced on the traffic intensity during various periods on regions in the Netherlands.

Do longer sequences improve the accuracy of identification of forensically important Calliphoridae species?

Species identification is a crucial step in forensic entomology. In several cases the calculation of the larval age allows the estimation of the minimum Post-Mortem Interval (mPMI). A correct identification of the species is the first step for a correct mPMI estimation. To overcome the difficulties due to the morphological identification especially of the immature stages, a molecular approach can be applied. However, difficulties in separation of closely related species are still an unsolved problem. Sequences of 4 different genes (COI, ND5, EF-1α, PER) of 13 different fly species collected during forensic experiments (Calliphora vicina, Calliphora vomitoria, Lucilia sericata, Lucilia illustris, Lucilia caesar, Chrysomya albiceps, Phormia regina, Cynomya mortuorum, Sarcophaga sp., Hydrotaea sp., Fannia scalaris, Piophila sp., Megaselia scalaris) were evaluated for their capability to identify correctly the species. Three concatenated sequences were obtained combining the four genes in order to verify if longer sequences increase the probability of a correct identification. The obtained results showed that this rule does not work for the species L. caesar and L. illustris. Future works on other DNA regions are suggested to solve this taxonomic issue.

Improving $K$-Subspaces via Coherence Pursuit

Subspace clustering is a powerful generalization of clustering for high-dimensional data analysis, where low-rank cluster structure is leveraged for accurate inference. $K$ -Subspaces (KSS), an alternating algorithm that mirrors $K$ -means, is a classical approach for clustering with this model. Like $K$ -means, KSS is highly sensitive to initialization, yet KSS has two major handicaps beyond this issue. First, unlike $K$ -means, the KSS objective is NP-hard to approximate within any finite factor for a large enough subspace rank. Second, it is known that the $\ell _2$ subspace estimation step is faulty when an estimated cluster has points from multiple subspaces. In this paper, we demonstrate both of these additional drawbacks, provide a proof for the former, and offer a solution to the latter through the use of a robust subspace recovery (RSR) method known as coherence pursuit (CoP). While many RSR methods have been developed in recent years, few can handle the case where the outliers are themselves low rank. We prove that CoP can handle low-rank outliers. This and its low computational complexity make it ideal to incorporate into the subspace estimation step of KSS. We demonstrate on synthetic data that CoP successfully rejects low-rank outliers and show that combining CoP with $K$ -Subspaces yields state-of-the-art clustering performance on canonical benchmark datasets.