Interval Estimation Method Research Articles

Inference of Short-Term Maximum Crack Width of RC Flexural Members Based on Interval Estimation Method

The design method based on observation and experimental model only considers the design of ultimate limit state in related research, the application range is not wide. This paper takes the short-term maximum crack width of RC flexural members as an example, puts forward the application of this method in the design of serviceability limit state. Dimensionless method was used to collect 4665 pieces of RC flexural member crack width data, and the normality test was performed to obtain the type of probability distribution obeyed by the crack width. Considering the situation without parameter information and known coefficient of variation, a direct estimation formula for short-term maximum crack width of RC flexural members based on interval estimation method is proposed.

Reference Interval Estimation from Mixed Distributions using Truncation Points and the Kolmogorov-Smirnov Distance (kosmic)

Appropriate reference intervals are essential when using laboratory test results to guide medical decisions. Conventional approaches for the establishment of reference intervals rely on large samples from healthy and homogenous reference populations. However, this approach is associated with substantial financial and logistic challenges, subject to ethical restrictions in children, and limited in older individuals due to the high prevalence of chronic morbidities and medication. We implemented an indirect method for reference interval estimation, which uses mixed physiological and abnormal test results from clinical information systems, to overcome these restrictions. The algorithm minimizes the difference between an estimated parametrical distribution and a truncated part of the observed distribution, specifically, the Kolmogorov-Smirnov-distance between a hypothetical Gaussian distribution and the observed distribution of test results after Box-Cox-transformation. Simulations of common laboratory tests with increasing proportions of abnormal test results show reliable reference interval estimations even in challenging simulation scenarios, when <20% test results are abnormal. Additionally, reference intervals generated using samples from a university hospital’s laboratory information system, with a gradually increasing proportion of abnormal test results remained stable, even if samples from units with a substantial prevalence of pathologies were included. A high-performance open-source C++ implementation is available at https://gitlab.miracum.org/kosmic.

Moisture Content in Decomposing, Desiccated, and Mummified Human Tissue

Due to the nature of mummification, tissue that has been significantly desiccated may never reach the advanced stages of decomposition. This research measured the moisture content of five donated individuals placed at the Forensic Anthropology Research Facility at Texas State University over a period of nearly three months. Moisture-content readings were taken from 20 sites on each body on a daily basis and then analyzed through multilevel modeling with temperature, humidity, solar radiation, and precipitation. This study found that desiccation appears to follow a consistent, asymptotic pattern and that temperature is the most influential environmental factor affecting moisture content. This study shows the viability for a postmortem interval estimation method based on accumulated degree-days and moisture content.

A two-step approach to interval estimation for continuous-time switched linear systems

Abstract This paper proposes a two-step interval estimation method for continuous-time switched linear systems subject to unknown but bounded disturbance and measurement noise. We first use an L∞ norm-based approach to attenuate the effect of uncertainties in observer design. Then, based on the obtained observer, interval estimation can be achieved via analyzing the bounds of estimation error. The proposed method is intuitive and independent of cooperativity constraint, which is main restriction of interval observer theory. The performance of the proposed method is demonstrated through a numerical simulation.

Fast interval estimation for discrete-time systems based on fixed-time convergence

This paper studies interval estimation for discrete-time linear systems with unknown but bounded disturbance and measurement noise. Inspired by the well-known parity space approach in the field of fault diagnosis, we propose a fast interval estimation method with fixed-time convergence property. A singular value decomposition-based parameter optimization algorithm is used to attenuate the effect of uncertainties on the estimation error. Comparison study illustrates the superiority of the proposed method over existing technique.

Rectal Temperature of Corpse and Estimation of Postmortem Interval.

Measurement of corpse temperature is mainly used for estimation of early postmortem interval, and rectal temperature is often used as a representative of body's core temperature in actual work because it is simple, quick and non-invasive. At present, the rectal temperature postmortem interval estimation method internationally accepted and widely used is HENSSGE's nomogram method, while many domestic scholars also deduced their own regression equations through a large number of case data. Estimation of postmortem interval based on rectal temperature still needs further study. The nomogram method needs to be optimized and extended, and quantification of its influencing factors needs to be dealt with more scientifically. There is still a lack of consensus on the probability and duration of the temperature plateau. There is no clear understanding of the probability and extent of the change in initial temperature caused by various causes. New methods and ideas enrich methodological research, but it still lacks systemicity and practicality. This article reviews the researches on estimation of postmortem interval based on rectal temperature in order to summarize the current situation of previous researches and seek new breakthrough points. Because the decline of body temperature can be easily influenced by many factors in vitro and vivo, and the influencing factors in different regions vary greatly, regionalization research and application may be a practical exploration to improve the accuracy of postmortem interval determination.

Evaluation method for HRRP imaging quality based on intuitionistic interval fuzzy set

Aimed at the performance evaluation of radar target recognition system based on high resolution range profile (HRRP), this paper proposes an evaluation method of HRRP quality based on intuitionistic interval fuzzy set under the background that HRRP has the characteristics of translation, attitude and velocity sensitivity. Compared with the general evaluation method, this method has two improvements. One is to extend the index value from single value to interval value by interval estimation method in mathematical statistics, which introduces sample variance into calculation to improve the utilization rate of sample information. The other is to construct intuitionistic fuzzy set by using correct recognition rate derivative sequence (DSCRR) and false recognition rate derivative sequence (DSFRR), which can make full use of experimental data and improve the reliability of experimental results. The distance calculation method of intuitionistic interval fuzzy set is used to get the distance between each scheme and the ideal scheme. Finally, the score of schemes which represent different HRRP imaging quality are obtained, and the performance evaluation is completed. The evaluation results obtained from simulation validation experiments are in accordance with objective facts, which proves the validity of this evaluation method.

Research on a diagnostic method of gear wear fault degree based on ultra‐complete independent component analysis

In this study, a diagnostic method for gear wear fault is proposed, which is based on the ultra-complete independent component analysis. The ultra-complete analysis model is constructed, and the similar source signals more than mixing signals are separated. Based on the typical features of the fault source signal, the useful component of the mixing signals can be found. As the fault source signal and similar fault source signal are similar, the magnification range of similar shapes can be estimated by using the interval estimation method. The mapping between one-way varying magnification time domain and rotary component fault degree is identified, and the fault degree judgment standard can be established. Thus, the fault degree can be determined. The method is used to deal with the state monitoring data of the gear, and the analysis results show that the diagnostic method is of practical value.

Uniform Inference in Panel Autoregression

This paper considers estimation and inference concerning the autoregressive coefficient ( ρ ) in a panel autoregression for which the degree of persistence in the time dimension is unknown. Our main objective is to construct confidence intervals for ρ that are asymptotically valid, having asymptotic coverage probability at least that of the nominal level uniformly over the parameter space. The starting point for our confidence procedure is the estimating equation of the Anderson–Hsiao (AH) IV procedure. It is well known that the AH IV estimation suffers from weak instrumentation when ρ is near unity. But it is not so well known that AH IV estimation is still consistent when ρ = 1 . In fact, the AH estimating equation is very well-centered and is an unbiased estimating equation in the sense of Durbin (1960), a feature that is especially useful in confidence interval construction. We show that a properly normalized statistic based on the AH estimating equation, which we call the M statistic, is uniformly convergent and can be inverted to obtain asymptotically valid interval estimates. To further improve the informativeness of our confidence procedure in the unit root and near unit root regions and to alleviate the problem that the AH procedure has greater variation in these regions, we use information from unit root pretesting to select among alternative confidence intervals. Two sequential tests are used to assess how close ρ is to unity, and different intervals are applied depending on whether the test results indicate ρ to be near or far away from unity. When ρ is relatively close to unity, our procedure activates intervals whose width shrinks to zero at a faster rate than that of the confidence interval based on the M statistic. Only when both of our unit root tests reject the null hypothesis does our procedure turn to the M statistic interval, whose width has the optimal N - 1 / 2 T - 1 / 2 rate of shrinkage when the underlying process is stable. Our asymptotic analysis shows this pretest-based confidence procedure to have coverage probability that is at least the nominal level in large samples uniformly over the parameter space. Simulations confirm that the proposed interval estimation methods perform well in finite samples and are easy to implement in practice. A supplement to the paper provides an extensive set of new results on the asymptotic behavior of panel IV estimators in weak instrument settings.

Maximum likelihood estimation for the proportion difference of two-sample binomial data subject to one type of misclassification

In this manuscript, we derived three likelihood-based interval estimation methods using a closed-form algorithm for the difference of two independent binomial proportion parameters with one type of misclassification. We acquired an identifiable model by using a double-sampling scheme. We also employed simulations to examine the robustness of our three likelihood-based interval estimation methods and summarize that our modified Wald method implemented to new data with Agresti-Coull type of adjustment performs well and has nominal coverage probabilities. This method was adapted to traffic data for an illustration.

Confidence interval estimation for the changepoint of treatment stratification in the presence of a qualitative covariate-treatment interaction.

The goal in stratified medicine is to administer the "best" treatment to a patient. Not all patients might benefit from the same treatment; the choice of best treatment can depend on certain patient characteristics. In this article, it is assumed that a time-to-event outcome is considered as a patient-relevant outcome and a qualitative interaction between a continuous covariate and treatment exists, ie,that patients with different values of one specific covariate should be treated differently. We suggest and investigate different methods for confidence interval estimation for the covariate value, where the treatment recommendation should be changed based on data collected in a randomized clinical trial. An adaptation of Fieller's theorem, the delta method, and different bootstrap approaches (normal, percentile-based, wild bootstrap) are investigated and compared in a simulation study. Extensions to multivariable problems are presented and evaluated. We observed appropriate confidence interval coverage following Fieller's theorem irrespective of sample size but at the cost of very wide or even infinite confidence intervals. The delta method and the wild bootstrap approach provided the smallest intervals but inadequate coverage for small to moderate event numbers, also depending on the location of the true changepoint. For the percentile-based bootstrap, wide intervals were observed, and it was slightly conservative regarding coverage, whereas the normal bootstrap did not provide acceptable results for many scenarios. The described methods were also applied to data from a randomized clinical trial comparing two treatments for patients with symptomatic, severe carotid artery stenosis, considering patient's age as predictive marker.

Interval Estimation Methods for Discrete-Time Linear Time-Invariant Systems

This paper investigates interval estimation methods for discrete-time linear time-invariant systems. We propose a novel interval estimation method by integrating robust observer design with reachability analysis. By introducing $H_{\infty }$ design into interval estimation, the proposed method is effective in improving the accuracy of interval estimation. Moreover, the comparisons and relationships between the existing methods and the proposed method are discussed in detail. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method.

Consistency Test of Ion-Flow Density Measurement System Using an Improved Gray Relational Analysis Method

It is known that the high-voltage direct-current (HVdc) transmission projects are increasingly used in actual electrical power transmission and the ion-flow field at ground level has been systematically studied. However, few studies focused on the research of the consistency among ion-flow density measurement systems (IDMS). IDMS should keep good concordance to discover the distribution law of ion-flow density in HVdc research. This study proposes an improved gray relational analysis (IGRA) method for consistency test of IDMS by introducing an entropy weight method and interval estimation method. Using this method, the consistency among IDMS is analyzed. The calibration assessment experiment was conducted and the results can be used for calibration assessment. The fault diagnosis experiment with +120 kV was also performed and findings show that the IGRA method is effective in finding potential fault for IDMS compared to the GRA method. Overall, results demonstrate that the IGRA method presented in this paper is useful for discovering distribution law of ion-flow density in HVdc research.

Interval estimation of actuator fault by interval analysis

This study proposes an interval estimation method of actuator fault for linear discrete-time system. An augmented descriptor system, which is equivalent to the original system, is formulated by letting the actuator fault term be an auxiliary state. Then, an H ∞ fault estimation observer is constructed to attenuate the effect of unknown but bounded disturbances and measurement noise. By using interval analysis technique, the interval estimation of actuator fault can be obtained. Finally, a DC servo-motor example is given to show the effectiveness and superiority of the proposed method.

Measurement error in continuous endpoints in randomised trials: Problems and solutions.

In randomised trials, continuous endpoints are often measured with some degree of error. This study explores the impact of ignoring measurement error and proposes methods to improve statistical inference in the presence of measurement error. Three main types of measurement error in continuous endpoints are considered: classical, systematic, and differential. For each measurement error type, a corrected effect estimator is proposed. The corrected estimators and several methods for confidence interval estimation are tested in a simulation study. These methods combine information about error‐prone and error‐free measurements of the endpoint in individuals not included in the trial (external calibration sample). We show that, if measurement error in continuous endpoints is ignored, the treatment effect estimator is unbiased when measurement error is classical, while Type‐II error is increased at a given sample size. Conversely, the estimator can be substantially biased when measurement error is systematic or differential. In those cases, bias can largely be prevented and inferences improved upon using information from an external calibration sample, of which the required sample size increases as the strength of the association between the error‐prone and error‐free endpoint decreases. Measurement error correction using already a small (external) calibration sample is shown to improve inferences and should be considered in trials with error‐prone endpoints. Implementation of the proposed correction methods is accommodated by a new software package for R.

Trajectory prediction of cylinders freely dropped into water in two dimensions based on state space model

Experiments show that the behavior of dropped cylindrical objects may be stochastic if small disturbances exist at the initial moment. An envelope of various trajectories can be obtained from experimental tests. In addition, numerical results also indicate that small changes of initial states, e.g., drop angle, rolling frequency, and the selection of drag coefficient, affect the trajectory greatly. If the initial state follows the Gaussian distribution,it can be considered as a nonlinear prediction problem. Then the Unscented Transformation (UT) method, as a deterministic sampling method, can be used to predict the trajectory. In this paper, we firstly present a systematic formulation of the cylinder's three-degree-of-freedom (3DOF) of motions as a state space model. Secondly, the UT method and the interval estimation method are employed to solve this prediction problem. Finally, the predicted trajectories will be compared with the experimental data. Meanwhile, two performance parameters are first proposed to evaluate the accuracy of predicted trajectory. Both parameters can be directly applied to the salvage and recovery of dropped objects in the offshore operations. In addition, the numerical results also show that the UT method is an efficient way to predict the trajectory of cylinders freely dropped into water. • The state space model of a dropped cylinder has been successfully constructed in this paper. • The trajectory of a dropped cylinder is considered as the stochastic process due to the uncertainty at the initial moment. • The Unscented Transformation (UT) method is employed to solve the nonlinear trajectory prediction. • Two performance measures R A and R L have been first proposed to evaluate the accuracy of numerical trajectory prediction, in order to meet the requirement from the offshore engineering industry.

A eukaryotic community succession based method for postmortem interval (PMI) estimation of decomposing porcine remains

Recent, short-term studies on porcine and human models (albeit with few replicates) demonstrated that the succession of the microbial community of remains may be used to estimate time since death. Using a porcine model (N=6) over an extended period of time (1703 ADD, or two months), this study characterized the eukaryote community of decomposing remains. Skin microbial samples were collected from the torso of each set of remains every day during the first week, on alternate days during the second week, and once a week for the remainder of the 60-day period; all collection intervals were recorded in accumulated degree days (ADD). The eukaryote community of each sample was determined using 18S ribosomal DNA (rDNA) MiSeq high throughput sequencing; data were analyzed in the Mothur pipeline (v1.39.5) and in IBM SPSS and R statistical packages. The relative abundance of eukaryote taxa across ADD/Days and an Analysis of Molecular Variance (AMOVA) indicated similarities between sequential ADD/Days, but significant differences in the eukaryote communities as broad stage ‘milestones’ of decomposition were reached. Fresh remains (0–57 ADD/0–2 Days; exhibiting a total body score (TBS) of 0–10) were characterized by the combined presence of Saccharomycetaceae, Debaryomycetaceae, Trichosporonaceae, Rhabditida, and Trichostomatia. During bloat and active decay (87–209 ADD/3–7 Days; exhibiting TBS of 11–20), Diptera was the most abundant eukaryotic taxa. During advanced decay stage (267–448 ADD/9–15 Days; exhibiting TBS of 21–25), Rhabditida was the most dominant eukaryote. Dry/skeletal remains (734–1703 ADD/26–61 Days; TBS≥26) were dominated by fungal families Dipodascaceae, Debaryomycetaceae, Trichosporonaceae, and Sporidiobolaceae. Using the family-level eukaryote taxonomic data for the entire study, random forest modelling explained 89.58% of the variation in ADD/Days, with a root mean square error (RMSE) of 177.55 ADD (≈6 days). Overall, these results highlight the importance of the microbial eukaryote community during the process of decomposition and in estimation of PMI.

Interval estimation of voltage magnitude in radial distribution feeder with minimal data acquisition requirements

The increased complexity of electric distribution networks, entails higher requirements on the interoperability between the information and communication technology (ICT) infrastructure and the power system. In current literature, distribution network monitoring methods require reliable operation of the ICT infrastructure, and depend on load estimation accuracy in cases of unavailable measurements. In the low voltage (LV) distribution network, ICT infrastructure is sensible to noise and congestion, and load estimation is challenged by irrational consumer behaviour. Such limitation impede the implementation of currently proposed methods. The conditions are complicated further when considering the impact of emerging data privacy concerns that can prevent utilization of household consumption data.Therefore, this work proposes a novel method for voltage magnitude interval estimation by utilizing the information from existing data acquisition systems in LV distribution networks. The proposed method is based on knowledge of load connection, and estimates the entire radial feeder voltage magnitude conditions from measurements of voltage magnitudes from minimum one node. The proposed method can therefore be implemented on existing ICT infrastructure and effectively keeps the privacy of consumers consumption profiles. The ability of the proposed method to estimate the feeder voltage interval is illustrated by an example and evaluated using Monte Carlo simulations for different meter distribution and accuracy scenarios.

Quantile-based reliability comparison of products: Applied to Log-normal distribution

In this paper, reliability comparison indices are developed for log-normally distributed populations. In this regard, three indices are proposed for reliability comparison of products respectively in earlier, later, and entire stages of their lifetime. These indices are based on the confidence interval of difference between quantiles of two independent lognormal distributions. Therefore, we apply a generalized confidence interval (GCIN), and a method of variance estimates recovery (MOVER) to construct confidence intervals for difference between quantiles. The coverage probability and average length of the proposed approaches are compared through Monte Carlo simulations. The proposed indices and interval estimation methods are applied to a real world case study to show their application and performance in life data analysis.

A novel method for post-mortem interval estimation based on tissue nano-mechanics.

Forensic estimation of post-mortem interval relies on different methods, most of which, however, have practical limitations or provide insufficient results, still lacking a gold standard method. In order to better understand the phenomenon of rigor mortis and its applicability to the post-mortem interval estimation, we decided to use atomic force microscopy, a tool often employed to measure mechanical properties of adherent cells. Thus, we surgically removed skeletal muscle samples of three forensic cases from 0 to 120h post-mortem and quantitatively evaluate two parameters: the Young's modulus (E), which gives information about the sample stiffness, and the hysteresis (H), which estimates the contribution of viscous forces. Despite being a preliminary study, the obtained results show that the temporal behavior of E well correlates with the expected evolution of rigor mortis between 0 and 48h post-mortem, and then monotonically decreases over time. Unfortunately, it is strongly affected by inter-individual variability. However, we found that H provides measurable data along a time-dependent curve back to the starting point, and these data measured on different subjects collapse onto a single master curve, getting rid of the inter-individual variability. Although a larger sampling should be performed to improve the result reliability, this finding is strongly suggestive that the evaluation of rigor mortis should involve the measure of the nanoscale dissipative behavior of muscular tissues.