Estimated Error Rate Research Articles

Low Rank and Structured Modeling of High-Dimensional Vector Autoregressions

Network modeling of high-dimensional time series data is a key learning task due to its widespread use in a number of application areas, including macroeconomics, finance and neuroscience. While the problem of sparse modeling based on vector autoregressive models (VAR) has been investigated in depth in the literature, more complex network structures that involve low rank and group sparse components have received considerably less attention, despite their presence in data. Failure to account for low-rank structures results in spurious connectivity among the observed time series, which may lead practitioners to draw incorrect conclusions about pertinent scientific or policy questions. In order to accurately estimate a network of Granger causal interactions after accounting for latent effects, we introduce a novel approach for estimating low-rank and structured sparse high-dimensional VAR models. We introduce a regularized framework involving a combination of nuclear norm and lasso (or group lasso) penalty. Further, and subsequently establish non-asymptotic upper bounds on the estimation error rates of the low-rank and the structured sparse components. We also introduce a fast estimation algorithm and finally demonstrate the performance of the proposed modeling framework over standard sparse VAR estimates through numerical experiments on synthetic and real data.

Discriminant analysis for discrete variables derived from a tree-structured graphical model

The purpose of this paper is to illustrate the potential use of discriminant analysis for discrete variables whose dependence structure is assumed to follow, or can be approximated by, a tree-structured graphical model. This is done by comparing its empirical performance, using estimated error rates for real and simulated data, with the well-known Naive Bayes classification rule and with linear logistic regression, both of which do not consider any interaction between variables, and with models that consider interactions like a decomposable and the saturated model. The results show that discriminant analysis based on tree-structured graphical models, a simple nonlinear method including only some of the pairwise interactions between variables, is competitive with, and sometimes superior to, other methods which assume no interactions, and has the advantage over more complex decomposable models of finding the graph structure in a fast way and exact form.

Joint Compressed Sensing and Enhanced Whale Optimization Algorithm for Pilot Allocation in Underwater Acoustic OFDM Systems

In underwater acoustic-orthogonal frequency division multiplexing (UWA-OFDM) systems, the performance of channel estimation is significantly affected by pilot allocation in the framework of compressed sensing (CS). However, for optimizing the pilot allocation, an exhaustive search method over all possible allocations is computationally prohibitive and random search method may not ensure convergence accuracy. In this paper, the meta-heuristic algorithm of the whale optimization algorithm (WOA) is employed to address this issue. For reinforcing the capability of balancing exploration and exploitation, an enhanced variant of WOA termed EWOA is presented with four optimization strategies. After that, a joint algorithm combining CS with EWOA (CS-EWOA) is proposed for pilot allocation in UWA-OFDM systems. Through extensive simulations, the improvement of EWOA is demonstrated on the majority of benchmark functions over other well-known meta-heuristic algorithms. With regard to bit error rate (BER) and mean square error (MSE) for channel estimation, the proposed CS-EWOA algorithm outperforms the equispaced, random, genetic algorithm (GA), particle swarm optimization (PSO), and WOA-based pilot allocation methods. Moreover, it is robust with varying system subcarriers and channel models. Furthermore, the CS-EWOA exhibits superior convergence performance without increasing the computational complexity compared with the GA-, PSO-, and WOA-based methods in the iteration process of pilot allocation optimization. It can be concluded from the simulation results that the proposed CS-EWOA algorithm is competitive to optimize pilot allocation for channel estimation in UWA-OFDM systems.

Image-Based Vehicle Speed Estimation

Vehicle speed is an important parameter that finds tremendous application in traffic control identifying over speed vehicles with a view to reducing accidents. Many methods, such as using RADAR and LIDAR sensors have been proposed. However, these are expensive, and their accuracy is not quite satisfactory. In this paper, a video-based vehicle speed determination method is presented. The method shows satisfactory performance on standard data sets and gives that error rate of velocity estimation is within 10%.

Joint Channel Estimation and Impulsive Noise Mitigation Method for OFDM Systems Using Sparse Bayesian Learning

The impulsive noise can deteriorate sharply the performance of orthogonal frequency division multiplexing (OFDM) systems. In this paper, we propose a novel joint channel impulse response estimation and impulsive noise mitigation algorithm based on compressed sensing theory. In this algorithm, both the channel impulse response and the impulsive noise are treated as a joint sparse vector. Then, the sparse Bayesian learning framework is adopted to jointly estimate the channel impulse response, the impulsive noise, and the data symbols, in which the data symbols are regarded as unknown parameters. The Cramer–Rao Bound is derived for the benchmark. Unlike the previous impulsive noise mitigation methods, the proposed algorithm utilizes all subcarriers without any a priori information of the channel and impulsive noise. The simulation results show that the proposed algorithm achieves significant performance improvement on the channel estimation and bit error rate performance.

Where Have All the Innocents Gone?

The DNA revolution has revealed that, contrary to received wisdom, the conviction of an innocent defendant by the vaunted American criminal justice system is far from a freakish event. The National Registry of Exonerations now lists more than 2,200 cases of wrongful convictions. Of course, 2,200 cases is a minuscule number compared to the roughly 1.5 million felons in state and federal prisons at any given moment. The last quarter century has seen a vigorous debate about the error rate that leads to the conviction of innocent defendants. Estimates have ranged from 0.027% to 15%; most estimates are in the 0.5% to 2% range. This Article presents the first study to draw on an existing data set of actual claims of innocence to estimate the overall error rate in the criminal justice system. The data come from the North Carolina Innocence Inquiry Commission, a unique program that allows applications for exoneration from convicted felons. Conservative assumptions about the North Carolina data set produce a likely error rate of 0.125% to 0.5%. Though the estimates of the wrongful conviction rate are thus limited to North Carolina, there is no reason to think that the error rate is materially different in other states. This Article is, in part, a response to Paul Cassell’s article, Overstating America’s Wrongful Conviction Rate? Reassessing the Conventional Wisdom About the Prevalence of Wrongful Convictions. Though the findings here as to the estimated error rate are higher than Professor Cassell’s, we do agree that previous estimates tend to be too high for reasons this Article will explore.

SPECMAR: fast heart rate estimation from PPG signal using a modified spectral subtraction scheme with composite motion artifacts reference generation.

The task of heart rate estimation using photoplethysmographic (PPG) signal is challenging due to the presence of various motion artifacts in the recorded signals. In this paper, a fast algorithm for heart rate estimation based on modified SPEctral subtraction scheme utilizing Composite Motion Artifacts Reference generation (SPECMAR) is proposed using two-channel PPG and three-axis accelerometer signals. First, the preliminary noise reduction is obtained by filtering unwanted frequency components from the recorded signals. Next, a composite motion artifacts reference generation method is developed to be employed in the proposed SPECMAR algorithm for motion artifacts reduction. The heart rate is then computed from the noise and motion artifacts reduced PPG signal. Finally, a heart rate tracking algorithm is proposed considering neighboring estimates. The performance of the SPECMAR algorithm has been tested on publicly available PPG database. The average heart rate estimation error is found to be 2.09 BPM on 23 recordings. The Pearson correlation is 0.9907. Due to low computational complexity, the method is faster than the comparing methods. The low estimation error, smooth and fast heart rate tracking makes SPECMAR an ideal choice to be implemented in wearable devices. Graphical Abstract Flow chart for the heart rate estimation using modified SPEctral subtraction scheme utilizing Composite Motion Artifacts Reference generation (SPECMAR) from photoplethysmographic (PPG) signals.

Context-specific interactions in literature-curated protein interaction databases

BackgroundDatabases of literature-curated protein-protein interactions (PPIs) are often used to interpret high-throughput interactome mapping studies and estimate error rates. These databases combine interactions across thousands of published studies and experimental techniques. Because the tendency for two proteins to interact depends on the local conditions, this heterogeneity of conditions means that only a subset of database PPIs are interacting during any given experiment. A typical use of these databases as gold standards in interactome mapping projects, however, assumes that PPIs included in the database are indeed interacting under the experimental conditions of the study.ResultsUsing raw data from 20 co-fractionation experiments and six published interactomes, we demonstrate that this assumption is often false, with up to 55% of purported gold standard interactions showing no evidence of interaction, on average. We identify a subset of CORUM database complexes that do show consistent evidence of interaction in co-fractionation studies, and we use this subset as gold standards to dramatically improve interactome mapping as judged by the number of predicted interactions at a given error rate.ConclusionsWe recommend using this CORUM subset as the gold standard set in future co-fractionation studies. More generally, we recommend using the subset of literature-curated PPIs that are specific to the experimental context whenever possible.

Detection of potential look-alike/sound-alike medication errors using Veterans Affairs administrative databases.

Results of a study to estimate the prevalence of look-alike/sound-alike (LASA) medication errors through analysis of Veterans Affairs (VA) administrative data are reported. Veterans with at least 2 filled prescriptions for 1 medication in 20 LASA drug pairs during the period April 2014-March 2015 and no history of use of both medications in the preceding 6 months were identified. First occurrences of potential LASA errors were identified by analyzing dispensing patterns and documented diagnoses. For 7 LASA drug pairs, potential errors were evaluated via chart review to determine if an actual error occurred. Among LASA drug pairs with overlapping indications, the pairs associated with the highest potential-error rates, by percentage of treated patients, were tamsulosin and terazosin (3.05%), glipizide and glyburide (2.91%), extended- and sustained-release formulations of bupropion (1.53%), and metoprolol tartrate and metoprolol succinate (1.48%). Among pairs with distinct indications, the pairs associated with the highest potential-error rates were tramadol and trazodone (2.20%) and bupropion and buspirone (1.31%). For LASA drug pairs found to be associated with actual errors, the estimated error rates were as follows: lamivudine and lamotrigine, 0.003% (95% confidence interval [CI], 0-0.01%); carbamazepine and oxcarbazepine, 0.03% (95% CI, 0-0.09%); and morphine and hydromorphone, 0.02% (95% CI, 0-0.05%). Through the use of administrative databases, potential LASA errors that could be reviewed for an actual error via chart review were identified. While a high rate of potential LASA errors was detected, the number of actual errors identified was low.

Using statistical models in estimating error rates for forensic firearm identification

Estimating error rates for firearm evidence identification is a fundamental challenge in forensic science. The recently developed Congruent Matching Cells (CMC) method provides applications to firearm evidence identification. To estimate error rates, appropriate statistical models are needed for the CMC values. In this paper, in addition to the binomial probability distribution the correlated binomial distribution is proposed. For an image comparison, the correlated binomial distribution can be applied to the cell pairs from CMC method. An application to an actual data set demonstrates that the correlated binomial distribution fits the relative frequency distribution of CMC values much better than the binomial distribution.

Cultural conformity generates extremely stable traditions in bird song

Cultural traditions have been observed in a wide variety of animal species. It remains unclear, however, what is required for social learning to give rise to stable traditions: what level of precision and what learning strategies are required. We address these questions by fitting models of cultural evolution to learned bird song. We recorded 615 swamp sparrow (Melospiza georgiana) song repertoires, and compared syllable frequency distributions to the output of individual-based simulations. We find that syllables are learned with an estimated error rate of 1.85% and with a conformist bias in learning. This bias is consistent with a simple mechanism of overproduction and selective attrition. Finally, we estimate that syllable types could frequently persist for more than 500 years. Our results demonstrate conformist bias in natural animal behaviour and show that this, along with moderately precise learning, may support traditions whose stability rivals those of humans.

Regularization and the small-ball method I: Sparse recovery

We obtain bounds on estimation error rates for regularization procedures of the form \begin{equation*}\hat{f}\in\mathop{\operatorname{argmin}}_{f\in F}(\frac{1}{N}\sum_{i=1}^{N}(Y_{i}-f(X_{i}))^{2}+\lambda \Psi(f))\end{equation*} when $\Psi$ is a norm and $F$ is convex. Our approach gives a common framework that may be used in the analysis of learning problems and regularization problems alike. In particular, it sheds some light on the role various notions of sparsity have in regularization and on their connection with the size of subdifferentials of $\Psi$ in a neighborhood of the true minimizer. As “proof of concept” we extend the known estimates for the LASSO, SLOPE and trace norm regularization.

Ultrasound and Dual-Energy X-Ray Absorptiometry Report Transcription Error Rates and Strategies for Reduction

PurposeRadiologists play an essential role in patient care by providing accurate and timely results. An error-free radiology report is an expectation of both patients and referring physicians. Software is currently available that can eliminate measurement and side types of errors while saving radiologists and sonographers time. The objectives of this study were to evaluate the potential reduction in report errors, estimate the potential time savings associated with implementation, and conduct a cost-benefit analysis of implementing two software programs. MethodsData on the number of measurement errors and side errors in ultrasound and dual-energy x-ray absorptiometry reports were collected, and the time required for data entry that the software would reduce was measured by report type. Generalized estimating equations regression was used to estimate error rates and data entry times and corresponding 95% confidence intervals by report type for radiologists and sonographers. Current wages and report volumes were then applied to the time savings to estimate the annual wage savings. Projected volume increases were applied to the annual estimates to generate a 5-year savings estimate. ResultsOverall, measurement errors occurred in 6% to 28% of ultrasound reports, depending on the report type. Side errors were rare. It was estimated that over 5 years, the software could save $693,777 in radiologist wages and $130,771 in sonographer wages, a total of $824,548 (range, $621,866-$1,039,714). ConclusionsThe use of data integration software would both significantly reduce errors in ultrasound and dual-energy x-ray absorptiometry reports and save a considerable amount of time and money.

A variational approach to atmospheric visibility estimation in the weather of fog and haze

Real-time atmospheric visibility estimation in foggy and hazy weather plays a crucial role in ensuring traffic safety. Overcoming the inherent drawbacks with traditional optical estimation methods, like limited sampling volume and high cost, vision-based approaches have received much more attention in recent research on atmospheric visibility estimation. Based on the classical Koschmieder’s formula, atmospheric visibility estimation is carried out by extracting an inherent extinction coefficient. In this paper we present a variational framework to handle the nature of time-varying extinction coefficient and develop a novel algorithm of extracting the extinction coefficient through a piecewise functional fitting of observed luminance curves. The developed algorithm is validated and evaluated with a big database of road traffic video from Tongqi expressway (in China). The test results are very encouraging and show that the proposed algorithm could achieve an estimation error rate of 10%. More significantly, it is the first time that the effectiveness of Koschmieder’s formula in atmospheric visibility estimation was validated with a big dataset, which contains more than two million luminance curves extracted from real-world traffic video surveillance data.

Blind Channel Estimation and Symbol Detection for Multi-Cell Massive MIMO Systems by Expectation Propagation

Massive MIMO systems exploit the favorable propagation condition of the radio channel, whereby the vector-valued channels between the base station (BS) and the terminals become mutually orthogonal. This property is used in a recently-proposed channel estimation method for multi-cell massive MIMO systems based on the eigenvalue decomposition (EVD) of the correlation matrix of the received vectors. In this paper, we present a blind channel estimation and symbol detection scheme for multi-cell massive MIMO systems based on expectation propagation (EP). The proposed algorithm is initialized with the channel estimation result from the EVD-based method. It is shown that in our EP formulation, channel estimation and symbol detection are “decoupled” in that EP iterations for channel estimation can be performed without the knowledge of the specific transmitted symbols. Therefore, channel estimation can be performed first followed by symbol detection. In particular, a liner symbol detection scheme such as zero-forcing (ZF) or minimum mean-squared error (MMSE) algorithm may be employed. Simulation results show that after a few iterations, the EP-based algorithm significantly improves the performance of the EVD-based method in both channel estimation and symbol error rate. Comparisons are also made with the results from a recently proposed blind detection scheme and it is shown that the proposed algorithm has better performance.

Superimposed signaling inspired channel estimation in full-duplex systems

Residual self-interference (SI) cancellation in the digital baseband is an important problem in full-duplex (FD) communication systems. In this paper, we propose a new technique for estimating the SI and communication channels in a FD communication system, which is inspired from superimposed signaling. In our proposed technique, we add a constant real number to each constellation point of a conventional modulation constellation to yield asymmetric shifted modulation constellations with respect to the origin. We show mathematically that such constellations can be used for bandwidth efficient channel estimation without ambiguity. We propose an expectation maximization (EM) estimator for use with the asymmetric shifted modulation constellations. We derive a closed-form lower bound for the mean square error (MSE) of the channel estimation error, which allows us to find the minimum shift energy needed for accurate channel estimation in a given FD communication system. The simulation results show that the proposed technique outperforms the data-aided channel estimation method, under the condition that the pilots use the same extra energy as the shift, both in terms of MSE of channel estimation error and bit error rate. The proposed technique is also robust to an increasing power of the SI signal.

Non-Orthogonal Multiple Access With Sequence Block Compressed Sensing Multiuser Detection for 5G

Compressed sensing-based multiuser detection (CSMUD) is a promising candidate to enable grant-free non-orthogonal multiple access for massive machine-type communication in the fifth-generation (5G) wireless communication system. Exploiting the sporadic node activity of the massive machine-type communication, CSMUD recovers the signal from far fewer linear measurements than the number of users, thereby facilitating massive connectivity. However, with the increase in the number of sensor nodes, the errors in the activity detection also increase. In this paper, we propose sequence block-based compressed sensing multiuser detection (SB-CSMUD), which enhances the activity detection by using block of multiple spreading sequences as signature of the node. It is demonstrated both empirically and theoretically that the SB-CSMUD scheme besides improving the detection error rate also increases the spectrum efficiency. Simulation results show that for the same least square estimation error rate (LSER), SB-CSMUD not only reduces the bandwidth by 10% but also reduces the bit error rate by more than 1.5 order of magnitude at signal-to-noise ratio (SNR) of 15 dB. The bandwidth can even be reduced by 25%–30% at SNR > 20 dB for achieving the same detection error rate at the cost of LSER. Furthermore, owing to the diversity in the spreading, the proposed scheme provides a more homogeneous performance for all the nodes. In addition, the computational complexity of the SB-CSMUD is the same as that of CSMUD at the only expense of the memory requirement for hosting the block sequence at the node.

Analysis of Cognitive Radio Capacity in Fading Channels

This research paper is intended to derive, simulate and analyze the capacity of cognitive radio (CR) system in fading channels. Capacity being an information theoretic perspective for a wireless system plays a role in the amount of information bits which can be transmitted. As wireless channel is subjected to multipath effects in a CR system caused by fading, capacity analysis needs to be done. Simulation carried out in terms of capacity, mean square error for channel estimation and bit error rate (BER) for the proposed system model of cognitive radio can give valuable information about performance of CR system in terms of data bits handling capacity. The amount of capacity in flat fading channels and frequency selective fading channels are simulated. The obtained results in terms of capacity can be used as reference for further analysis to be explored relating to design of cognitive radio systems for developing applications for 5G systems.

Nurses’ Medication Administration Errors at Medical Surgical Units

Medication administration errors (MAEs) are among the key concepts of patient safety in clinical care settings that have long been the focus of study and exploration because they contribute directly to patient injury, death and health care costs. Medication errors are recurrent and expected to be a prolonged problem in the health care system. The administration of medication is predominantly an important part of nursing practice that has a dimension of quality of care and organizational performance. The study sought to assess nurses’ medication administration errors at the general medical and surgical units. A descriptive cross sectional study design was used where stratified random sampling using the medical and surgical units as strata was used to proportionately recruit 100 Nurses. Each nurse was further observed twice during medication administration process making a total of 200 observations included in the study. Two tools were used to collect data for the current study. The interviewer administered medication administration errors questionnaire and a concealed medication administration observation checklist. Data was collected for a period of four months and analyzed using descriptive and inferential statistics to check for relations between variables. The study findings imply that the wrong rate of administration, the wrong time of administration and medication being administered after the order to discontinue was written were the highly perceived MAEs reported respectively as occurring ‘most of the time’ by 27%, 23% and 15% of the studied nurses. For observed MAEs, the wrong time error type had the highest estimated error rate of 51%, followed by the documentation error at 29% and technique error at 27.5%. There was statistical significant difference between the pharmacy reasons subscale of causes of MAEs and the age (F= 5.465, p=0.006), clinical experience (F=3.922, p=0.011) and type of shift (F=2.507, p=0.035) the nurse works most. Further, there was statistically significant mean differences between the medical and surgical units with regard to the medication packaging subscale (t=4.160, p=0.044). The findings also revealed negative significant correlation between the observed MAEs scores and the nurses’ reported scores on types of IV & non-IV MAEs (r_s=-0.266, p=0.007) and the pharmacy reasons subscale (r_s=-0.266, p=0.046). Updating, developing, disseminating and implementing medication administration guidelines and protocols in the hospital settings is required. Nurses have to embrace the occurrence of MAEs as a patient safety indicator that should be viewed as an opportunity to learn and prevent MAEs through rationalized medication management protocols and guidelines.

Estimating error rates for firearm evidence identifications in forensic science

Estimating error rates for firearm evidence identification is a fundamental challenge in forensic science. This paper describes the recently developed congruent matching cells (CMC) method for image comparisons, its application to firearm evidence identification, and its usage and initial tests for error rate estimation. The CMC method divides compared topography images into correlation cells. Four identification parameters are defined for quantifying both the topography similarity of the correlated cell pairs and the pattern congruency of the registered cell locations. A declared match requires a significant number of CMCs, i.e., cell pairs that meet all similarity and congruency requirements. Initial testing on breech face impressions of a set of 40 cartridge cases fired with consecutively manufactured pistol slides showed wide separation between the distributions of CMC numbers observed for known matching and known non-matching image pairs. Another test on 95 cartridge cases from a different set of slides manufactured by the same process also yielded widely separated distributions. The test results were used to develop two statistical models for the probability mass function of CMC correlation scores. The models were applied to develop a framework for estimating cumulative false positive and false negative error rates and individual error rates of declared matches and non-matches for this population of breech face impressions. The prospect for applying the models to large populations and realistic case work is also discussed. The CMC method can provide a statistical foundation for estimating error rates in firearm evidence identifications, thus emulating methods used for forensic identification of DNA evidence.