Statistical Parameter Estimation Research Articles

Estimating survival parameters under conditionally independent left truncation.

Databases derived from electronic health records (EHRs) are commonly subject to left truncation, a type of selection bias that occurs when patients need to survive long enough to satisfy certain entry criteria. Standard methods to adjust for left truncation bias rely on an assumption of marginal independence between entry and survival times, which may not always be satisfied in practice. In this work, we examine how a weaker assumption of conditional independence can result in unbiased estimation of common statistical parameters. In particular, we show the estimability of conditional parameters in a truncated dataset, and of marginal parameters that leverage reference data containing non‐truncated data on confounders. The latter is complementary to observational causal inference methodology applied to real‐world external comparators, which is a common use case for real‐world databases. We implement our proposed methods in simulation studies, demonstrating unbiased estimation and valid statistical inference. We also illustrate estimation of a survival distribution under conditionally independent left truncation in a real‐world clinico‐genomic database.

РАЗРАБОТКА УТОЧНЕННОГО Р-БЛОКА КАК МОДЕЛИ СЛУЧАЙНОЙ ВЕЛИЧИНЫ В ЗАДАЧА Х АНАЛИЗА НАДЕЖНОСТИ СТРОИТЕЛЬНЫХ КОНСТРУКЦИЙ

The article describes a new type of the p-box (probability box) for random variables modeling in problems of structural reliability analysis. The advantage of the presented random variable model is that it is based on confidence estimates of statistical parameters without the need to confirm the hypothesis about the type of probability distribution function. The numerical example shows that the introduction of additional information in the form of a median’s confidence interval to the p-boxes based on the Chebyshev’s inequality is leading to reducing the area between the boundary distribution functions of the random variable. This makes it possible to reduce the level of uncertainty during further arithmetical operations with p-boxes in problems of structural reliability analysis, which is clearly visible after the discretization of the p-boxes into the Dempster-Shafer structure.

Robust modeling of multivariate longitudinal data using modified Cholesky and hypersphere decompositions

Due to the convenience of the statistical interpretation and parameter estimation, a normal distribution is typically assumed for multivariate longitudinal data analysis. However, this assumption may be questionable in practice, because it is possible that outliers exist or that the underlying data will show heavy tails. In addition, since the covariance matrix should explain complex correlation structures, it must be positive-definite, and as it is also high-dimensional, the modeling of the covariance matrix is not easy. To solve these problems, we propose the robust modeling of multivariate longitudinal data by considering multivariate t distribution, and we exploit modified Cholesky and hypersphere decompositions to model the covariance matrix. The estimation of the models is shown to be robust when the data include outliers and exhibit heavy tails. The performance of our proposed model and algorithm is illustrated using a nonalcoholic fatty liver disease data set and several simulation studies.

Single-molecule characterization of the iLID system in live bacterial cells

The development of single-molecule tracking (SMT) microscopy has enabled high resolution, live-cell imaging of protein motion in real time. The diffusion rate of the tracked protein can be used to deduce protein-protein interactions at specific subcellular locations, because diffusion is dependent on the size of the protein complex. However, the assignment and validation of diffusive states to specific oligomeric species relies on statistical parameter estimation and, in many cases, deletion or mutation of binding partners that disrupt the protein-protein interaction in question.

Using (ECM) Algorithm to Estimate the Missing Values and Make Comparison between (MLE) and (GA) Algorithm for Estimating Parameters of Multivariate Skew Normal Distribution (MSN)

The estimation of statistical parameters for multivariate data leads to waste in the information if the missing values are neglected, which will subsequently lead to inaccurate estimates. Therefore, the incomplete data must be estimated using one of the statistical estimation methods to obtain accurate results and thus obtaining good estimates for the parameters.The aim of this paper is to estimate the missing values for the multivariate skew normal distribution function using the Expectation Conditional Maximization (ECM) algorithm. After estimating the missing values, the parameters are estimated using Maximum Likelihood Estimation (MLE) with the Newton-Raphson algorithm, as well as using the Genetic Algorithm (GA). Using simulation, the Mean Squared Error (MSE) was calculated to find out which method is the best for estimation by comparing the two methods using different sample sizes (400, 600, and 800). The (GA) that is based on the (ECM) algorithm to estimate the missing values proved to be better and more efficient than the (MLE) method in terms of the results.

Influence of Risk Analysis as a Risk Management Practice on Project Performance in Kenya Commercial Banks

Undertaking an information technology project in a banking environment is a complex task. Studies globally and locally indicate a high failure rate of information technology projects. Standish group report 2019 states that 83.9 percent of information technology projects partially or completely fail. The majority of projects 52 percent were over budget, overdue, or lacked promised functionality. Previous studies indicate information technology projects in commercial Banks in Kenya experience the same project performance variations, as projects are either delayed, over budget or have issues with functionality. Risk is a factor that challenges project performance. Project risk management includes risk identification, analysis, response, and monitoring and control of risk in a project. The overall goal of this study was to examine the relationship of risk management and information technology project performance in Kenyan commercial banks, taking into account the moderating effect of project complexity and the mediating effect of risk culture, both of which had been largely overlooked in previous research hence filling a research vacuum. The target population was forty projects in Kenyan commercial banks in Kenya which made the unit of analysis. Stratified and simple random sampling technique was used. Stratified and simple random sampling technique was used. Questionnaires were used to collect the data from the targeted one hundred and eight respondents. The instrument was tested for reliability by use of Cronbach's alpha coefficient of internal consistency test and validity by use of selected information technology project professionals' review. Drop and pick method of administering questionnaires was used so as to allow respondents enough time to go through the questionnaires and give their responses. An option of online questionnaires was also available to respondents. Based on a survey, the research used both descriptive and explanatory analysis designs. The association between risk analysis and performance of information technology projects in Kenyan commercial banks was investigated using multiple regression. Quantitative data was analyzed using multiple regression analysis model software tool SPSS Version 25. The study adopted empirical model of least squares method while testing the hypotheses. The researcher conducted diagnostic tests of Normality, Linearity, Homoscedasticity and Multicollinearity to see if the data conforms to the basic assumptions of linear regression. The findings were presented using statistical parameter estimates. Tables and figures were used to present data, and supported by explanatory annotations. The results indicated that risk analysis had significant effects on the performance of information technology projects in the banking sector. The study recommends that banks should consider implementing and fully operationalize risk analysis in information technology projects. The Central Bank of Kenya should also consider putting in place an information technology projects risk policy framework to aid the banks in project undertakings. The finding in this research will aid project managers and different stakeholders in the banking and related sectors in managing information technology projects risks and hence increase the success rate of the projects. The knowledge gap is also addressed by scholarly work that has resulted from this research by providing statistical data analysis and explanations on the IT project performance in relation to risk analysis influence in commercial banks in Kenya.

Зональні ландшафтно-геохімічні особливості територій плодових насаджень України

There are possibilities of using background values, concentration clark and coefficients of biogeochemical specialization of soils for the selection of agrochemical measures and evaluation of the quality of fruit crops are indicated. The research was conducted on five agro-landscapes of fruit crops in four fruit-growing zones of Ukraine and characterized the distribution in the soil of 12 micronutrients (Zn, Co, Cu, Mo, Mn, Sr, Pb, Cr, Ni, V, Ba, Ti). It is noted that the indicator of zonal features of agrolandscapes of fruit crops is the spatial differentiation the statistical estimates of landscape-geochemical parameters, among which medians of samples (Ме) allow for the most informative and concise assessment of them. It was established that the zonal differences in the background content of Mo, Zn, Cu, Co, Sr in the soils of the territories of fruit plantations fluctuate most significantly with variation from 3 to 8 times, while Mn, Pb, Cr, Ni, V, Ba, Ti do not exceed 2, 5 times. Values of concentration clark greater than 1.5 indicate the geochemical concentration of Pb in the soils of all fruit-growing areas of Ukraine, Cu - in the Polissia zone, Mo - in the Western Forest Steppe, Zn, Cu, Co - in the Western and Central Steppe, Cu, Mo, Sr, Zn - in the Southern Steppe . This determines the expediency of minimal use of appropriate microfertilizers in the specified territories, as well as the regional need for limited use of lead preparations. Biogeochemical imbalance of nutritional microelements is manifested in each zone of fruit growing. It is characterized by an excess of Cu and a lack of Co, Mo in Polissia, an excess of Zn, Mo in the Western Forest Steppe, an excess of Zn, Cu in the Western and Central Steppe, an excess of Zn, Cu, Sr in the Southern Steppe. This indicates the possibility of widespread development of non-infectious phytopathologies, both an excess and a lack of nutrient microelements in fruit growing products in most territories of Ukraine.

Synthesis of the optimal algorithm and structure of contactless optical device for estimating the parameters of statistically uneven surfaces

The production of parts and (or) finished products in electronics, mechanical engineering and other industries is inextricably linked with the control of the accuracy and cleanliness of the processed surfaces. Currently existing meters of parameters of statistically uneven surfaces, both contact and non-contact have some disadvantages, as well as limitations due to methods and design features of measurement. Speckle interferometric methods for measuring parameters of statistically uneven surfaces make it possible to get away from some disadvantages inherent in existing methods and measurements. The use of methods of statistical radio engineering, methods of optimization of statistical solutions and estimates of parameters of predictive distributions for optimal radio engineering system synthesis is promising for the analysis and processing optical-electronic coherent laser space-time signals (speckle images) form with the laser radiation scattered by statistically uneven surfaces. This work synthesizes the optimal algorithm and structure for analyzing the parameters of statistically-temporal surfaces based on spatio-temporal processing of optical speckle interference signals and images using modern methods of optimal synthesis of radio engineering and coherent optoelectronic systems. In this work, an algorithm for processing optical signals scattered by statistically uneven surfaces is synthesized and investigated for problems of optimal estimation of parameters and statistical characteristics of statistically uneven surfaces. A block diagram of the optical contactless device for evaluating the parameters of statistically uneven surfaces is proposed. The limiting errors of estimation parameters of statistically uneven surfaces and the optimal installation angles of the emitters and the optical receiver are investigated. Equations are obtained for estimating the root-mean-square height of the ridges and the correlation radius of small-scale statistically uneven surfaces in the approximation of small perturbations. The proposed method for evaluating the parameters of statistically uneven surfaces allows to increase the accuracy of measurements, to conduct a non-contact assessment of the parameters - even statistically uneven surfaces that have geometric surface irregularities or located in hard-to-reach places, for example, grooves, holes, as well as products of cylindrical, spherical and other shapes.

Proximity of Bachelier and Samuelson Models for Different Metrics

This paper proposes a method of comparing the prices of European options, based on the use of probabilistic metrics, with respect to two models of price dynamics: Bachelier and Samuelson. In contrast to other studies on the subject, we consider two classes of options: European options with a Lipschitz continuous payout function and European options with a bounded payout function. For these classes, the following suitable probability metrics are chosen: the Fortet-Maurier metric, the total variation metric, and the Kolmogorov metric. It is proved that their computation can be reduced to computation of the Lambert in case of the Fortet-Mourier metric, and to the solution of a nonlinear equation in other cases. A statistical estimation of the model parameters in the modern oil market gives the order of magnitude of the error, including the magnitude of sensitivity of the option price, to the change in the volatility.

Parameter estimation with improved model prediction for over-parametrized nonlinear systems

Parameter estimation is a crucial step in system modeling. In practice, the fundamental models developed by chemical engineers are often complex. There may be parameters that have no or little effect on the model prediction. Also, some parameters may have an impact on model prediction related to other parameters. In this case, it is difficult to estimate all the parameters. The general approach to address this issue is to select a subset of parameters to estimate, and fix the rest at the initial value. Here, a reduced Hessian and statistical criterion-based parameter estimation approach is proposed for improving model prediction. The proposed method considers the influence of the initial parameter values on model prediction. In the process of selecting the optimal subset of transformed parameters based on the mean square error (MSE) of output, the sensitivity matrix is replaced by the reduced Hessian matrix, which can save computational cost of MSE calculation. After obtaining the optimal transformed parameter subset, the optimal initial values are selected based on the statistical criterion. The large difference between the model prediction and the actual model output caused by the arbitrary initial values can be avoided. The numerical results show the effectiveness of the proposed approach.

Statistical parameter estimation of multimode multiphoton-subtracted thermal states of light

Thermal states of light are widely used in quantum optics for various quantum phenomena testing. Particularly, they can be utilized for characterization of photon creation and photon annihilation operations. During the last decade the problem of photon subtraction from multimode quantum states become of much significance. Therefore, in this work we present a technique for statistical parameter estimation of multimode multiphoton subtracted thermal states of light, which can be used for multimode photon annihilation test.

Semi-automated Estimation of Reliability Measures from MaintenanceWork Order Records

Determining mean-time-between-failure (MTBF) estimation for in-service assets is an essential process. Statistical distributions of end-of-life values are used to assess asset reliability performance and the effectiveness of maintenance strategy. However, identifying the end-of-life event for each instance of functional failure is an arduous, manual process dependent on structured and unstructured fields in the maintenance management system and rules used by individual reliability engineers. We emulate the process of end-of-life event detection using a natural language processing pipeline followed by statistical parameter estimation to produce MTBF values for in-service assets from maintenance work order data. Using this pipeline we test how alternate mappings of words in unstructured text and the use, or not, of structured data can impact the identification of end-of-life events. We demonstrate the pipeline on data sets from two industrial users with 14,508 and 89,259 maintenance work orders, respectively. We find that resulting MTBF estimates can vary from, for example, 97 to 226 days for a single asset depending on the mappings and rules used. The main contributions of this paper are a) a demonstration of the impact of undocumented decisions made by reliability engineers in identifying end-of-life events on MTBF estimates, and b) an end to end pipeline for MTBF estimation from raw maintenance work order texts. In addition, we provide open-source code for the pipeline that can be used by asset owning organisations to semi-automate the MTBF estimation process in a manner that is fast and scalable, and ensures the rules used for end of life determination are documented and hence the process is transparent and repeatable.

Study of Synergistic Effects in Complex Stochastic Systems

In this paper, a method for detecting synergistic effects of the interaction of elements in multi-element stochastic systems of separate redundancy, multi-server queuing, and statistical estimates of nonlinear recurrent relations parameters has been developed. The detected effects are quite strong and manifest themselves even with rough estimates. This allows studying them with mathematical methods of relatively low complexity and thereby expand the set of possible applications. These methods are based on special techniques of the structural analysis of multi-element stochastic models in combination with majorant asymptotic estimates of their performance indicators. They allow moving to more accurate and rather economical numerical calculations, as they indicate in which direction it is most convenient to perform these calculations.

Robust estimation for Binomial conditionally nonlinear autoregressive time series based on multivariate conditional frequencies

A topical problem of robust statistical estimation of parameters for binomial conditionally nonlinear autoregressive (BiCNAR) time series under innovation outliers is considered. This problem is solved by means of s-order Markov properties for observed time series and probabilistic properties of multivariate conditional frequencies of the future state under its s-prehistory. The new robust statistical estimator ζˆ called frequencies-based estimator (FBE) is constructed for the BiCNAR parameters under innovation outliers with arbitrary discrete probability distribution having some fixed known expectation. Under mild regularity conditions the constructed FBE is shown to have the robustness properties: consistency and asymptotic normality with obtained asymptotic covariance matrix. FBE also has computational advantages: an explicit form and a fast recursive re-estimation algorithm for extension of the model. Asymptotic risk functional and its minimum are evaluated using Fisher information matrix for the considered model. Sensitivity analysis of the statistical estimator ζ̃ for the BiCNAR parameters, that is constructed for the hypothetical model without outliers, is carried out for the situation with innovation outliers: ζ̃ is shown to be inconsistent in this situation, its bias and the instability coefficient are evaluated and analyzed. The robust estimator ζˆ has a free parameter — weight matrix H. The optimal weight matrix H∗ is found by minimization of the asymptotic risk w.r.t. H. Statistical estimator for H∗ based on the observed time series is constructed. Results of multiple computer experiments on simulated and real data illustrate the theory.

The comparison of descriptive statistical parameter estimation stability using raw scores and rasch model

This research aims at analyzing the comparison of descriptive statistical Parameter Estimation stability using raw scores and Rasch model. The empirical data were the responses of the 12th Grade Science Students of Senior High School on Science Literary Test based on the integrated mathematics and natural sciences conducted at SMAN 2 and SMAN 3 Tegal, Central Java. This research employed a bootstrapping method assisted with SPSS version 21, while the Rasch model was assisted with R program version 3.6.3 eRm package Version 1.0.1. The parameter stability estimation was seen from its error standard and bias scores. The scores using the Rasch model was proven giving higher stability when compared to that using the raw scores in its descriptive statistical parameter estimation both from its error standard and bias aspects. Based on the error standard used, it showed that the mean and the standard deviation estimation when using the Rasch model scores was around 8 times more stable when compared to that when using the raw scores, while its median estimation were 16-18 times. Due to the use of bias measurement, it showed that the mean estimation when using the Rasch model scores was 6-10 times more stable when compared to that using the raw scores, while its median estimation and standard deviation were respectively 43-282 times and 7-10 times more stable.

Pengembangan Simulasi Kejadian Diskret Berbasis Paket Simmer pada R

This study aims to describe the various capabilities of the simmer package on R, especially in running a discrete event simulation model, then develop a DES simulation model building technique, which is effective and can represent real systems well, and explore the simulation output on this simmer, both in statistical summary form and parameter estimation. The method used in this research is the literature study, with descriptive and exploratory approaches. Model development is more effective when it is carried out starting from simple models, to more complex forms step by step, and describing the system using a flow chart. Replication for simulations is easy to perform, so as to get standard error values ​​for model parameter estimators. The stages in developing a discrete event simulation model with a simmer, start with compiling a simple flowchart to a more complex form, and replication is carried out. The simmer output in the form of data.frame makes it very easy to further process the output. The simple R API on simmer will also make it easier to simulate

Revival and recent advancements in the spatial fishery models originally conceived by Sidney Holt and Ray Beverton

Abstract Sidney Holt and Ray Beverton are primarily recognized for developing the basis of demographic stock assessment modelling, but their enduring legacy continues to influence and guide advancements in many fields of fisheries science. Although largely forgotten, their contributions to spatial modelling laid the foundation for a variety of applications in aquatic and terrestrial populations. Spatial modelling approaches are rapidly evolving beyond even the visionary scope of Beverton and Holt due to advancements in understanding of spatial population structure, collection of spatially explicit data, and statistical parameter estimation. A review of Beverton and Holt’s original movement models demonstrates that understanding the origins and basic underlying assumptions can help ensure that current models are consistent with fundamental principles. Additionally, recent simulation studies show that conforming to or revising spatial model assumptions is essential for accurate estimation. As fisheries science transitions to more complex spatial stock assessment models, understanding their conceptual development and the lessons learned by our predecessors is essential for proper model specification and application.

DATA FILTERING TECHNIQUES IN DECISION SUPPORT SYSTEMS

Background. The majority of modern dynamic processes in economy, finances, ecology, technologies and many other areas of studies exhibit short- and long-term nonlinear and nonstationary behavior. That is why it is required to create for their thorough analysis modern highly developed specialized instrumentation providing for appropriate preliminary statistical data processing, simulation state and parameter estimation and quality forecasting their evolution in time to be used in decision support systems (DSS). Objective. The purpose of the paper is to perform introductory analysis of some modern methods for filtering statistical and experimental data; to consider modern filtering techniques on the basis of probabilistic Bayesian approach, that provide a possibility for preparing the data to adequate simulation, computing high quality state and forecast estimates for dynamic systems in stochastic environment and availability of measurement errors. Methods. To implement modern data filtering techniques appropriate simulation and optimization procedures, probabilistic Bayesian methods of data analysis are utilized; simulation algorithms for parameter estimation, and criteria bases for analyzing quality of intermediate and final results in the frames of DSS are used. Results. A set of data filtering techniques is presented to be used together with the models describing formally selected processes dynamics. The methodology is considered for implementation of probabilistic Bayesian filter based upon modern statistical data analysis techniques including application of appropriate simulation procedures. Conclusions. Development of effective means for simulation, state estimation and forecasting dynamics of nonlinear nonstationary processes in various areas of human activities provides a possibility for high quality state and parameter estimation and compute short and middle term forecasts for their future evolution. The methods of optimal Kalman and probabilistic Bayesian filtering considered in the review provide a possibility for performing appropriate analysis of nonlinear nonstationary processes, compute forecasts and provide for managerial decision support on the basis of the forecast estimates.

Current Bioequivalence Study Designs in South Korea: A Comprehensive Analysis of Bioequivalence Study Reports Between 2013 and 2019.

Demonstration of bioequivalence (BE) is mandatory while developing generic drugs. The scientific concept of BE applies equally to different regulatory agencies. However, the application of the concept may differ for each agency, which can affect the design of BE studies. To evaluate the study practices in terms of the BE concept in South Korea, we retrospectively analyzed BE study reports available from Ministry of Food and Drug Safety between 2013 and 2019. Statistical estimation of the pharmacokinetic parameters, including peak concentration and area under the concentration–time curve to the last measurable concentration, as well as study design, number of subjects in a study, study duration, fasting status, and formulation of specific drugs were obtained. The drugs were classified per World Health Organization Anatomical Therapeutic Chemical Classification and Biopharmaceutics Classification System. Post-hoc intrasubject coefficient of variation and corresponding sample sizes were calculated from the 90% confidence intervals of pharmacokinetic parameters. A total of 143 generic drugs in 588 BE studies were analyzed. The largest number of studies were performed in the area of Cardiovascular system (172 studies), followed by Nervous system (143 studies) and Alimentary tract and metabolism (92 studies). Overall, BE studies in South Korea were conducted in accordance with the global guideline despite the differences in details. BE studies were focused on the several therapeutic areas and conducted in a similar manner. The number of subjects was generally larger than that estimated with 90% power.

A novel DNN tracking algorithm for structural system identification

In the field of structural health monitoring (SHM), cameras record videos and tracking methods can be applied to calculate the structural displacement. Commercial and unmanned aerial vehicle (UAV) cameras are promising non-contact sensors owning to their high availability and easy installation. However, effective tracking methods need to be developed. In this study, we firstly propose an end-to-end vision measuring framework with a novel deep neural network (DNN) tracker, named Siamese Single Decoder Network (SiamSDN). The system requires no target installation and uses cellphone cameras. For SiamSDN, the position and scale of bounding box are formulated through statistical parameter estimation. Unlike generative trackers, SiamSDN does not require manually extracted features or pre-defined motion areas. The tracking object is solely identified in the first frame. A shaking table test of a five-storey structure is carried out to demonstrate the efficiency. Besides, a UAV is used to simulate the field test. To minimize the error caused by the vibrations of UAV, digital video stabilization (DVS) is proposed to eliminate the drifts. Videos taken by both the commercial and UAV cameras are analyzed to calculate the displacements. Comparing our DNN tracker with feature point matching approach, SiamSDN improves the displacement measuring accuracy by 66.16% and 57.54%, respectively, and the frequency characteristics are obtained precisely.