Search

The Ordinary Least Square (OLS) estimator remains Best Linear Unbiased Estimator (BLUE) when all the assumptions surrounding it stay intact, but at an iota of violation of the assumptions, it becomes inefficient and unstable. Some causes of the violation are the multicollinearity and the presence of extreme values (outliers). Recently robust Kibria -Lukman based on M estimator was proposed by Majid et al. (2022) but when there are outlying cases in the y-direction. Since, outliers in the x-direction may be inevitable in the data set, therefore it becomes imperative to examine the performance of the robust-M Kibria-Lukman (KL-M) estimator as alternative to already proposed robust estimators that can handle these problems when there are outliers in the x-direction. Through the Monte Carlo experiment, theoretical results under some conditions and factors, including application to real-life data, the new estimator outperformed other estimators considered in this study in the presence of multicollinearity and extreme values in the x-direction. As the error variances (σ2), level of multicollinearity (rho) and percentage (px), and magnitude (mx) of outliers increase, the Mean Square Error (MSE) of the estimators' considered increase. Meanwhile the MSEs of the estimators decrease as the sample size (n) increases. When rho>0, mx>0, the (px) increases, and sample size (n) increases KL-M along sides, ordinary Kibria-Lukman (KL) estimator outperformed other estimators as the two anomalies occur simultaneously. The KL-M performed better, especially when the sample size was n=100. Conclusively, at the different biasing parameters of the estimators, KL-M performed better than other estimators considered in the study. In the same vein, real-life data was adopted to affirm the claim. [ABSTRACT FROM AUTHOR]

Causal decomposition analysis is among the rapidly growing number of tools for identifying factors ("mediators") that contribute to disparities in outcomes between social groups. An example of such mediators is college completion, which explains later health disparities between Black women and White men. The goal is to quantify how much a disparity would be reduced (or remain) if we hypothetically intervened to set the mediator distribution equal across social groups. Despite increasing interest in estimating disparity reduction and the disparity that remains, various estimation procedures are not straightforward, and researchers have scant guidance for choosing an optimal method. In this article, the authors evaluate the performance in terms of bias, variance, and coverage of three approaches that use different modeling strategies: (1) regression-based methods that impose restrictive modeling assumptions (e.g., linearity) and (2) weighting-based and (3) imputation-based methods that rely on the observed distribution of variables. The authors find a trade-off between the modeling assumptions required in the method and its performance. In terms of performance, regression-based methods operate best as long as the restrictive assumption of linearity is met. Methods relying on mediator models without imposing any modeling assumptions are sensitive to the ratio of the group-mediator association to the mediator-outcome association. These results highlight the importance of selecting an appropriate estimation procedure considering the data at hand. [ABSTRACT FROM AUTHOR]

Entropy estimation is a fundamental problem in information theory that has applications in various fields, including physics, biology, and computer science. Estimating the entropy of discrete sequences can be challenging due to limited data and the lack of unbiased estimators. Most existing entropy estimators are designed for sequences of independent events and their performances vary depending on the system being studied and the available data size. In this work, we compare different entropy estimators and their performance when applied to Markovian sequences. Specifically, we analyze both binary Markovian sequences and Markovian systems in the undersampled regime. We calculate the bias, standard deviation, and mean squared error for some of the most widely employed estimators. We discuss the limitations of entropy estimation as a function of the transition probabilities of the Markov processes and the sample size. Overall, this paper provides a comprehensive comparison of entropy estimators and their performance in estimating entropy for systems with memory, which can be useful for researchers and practitioners in various fields. [ABSTRACT FROM AUTHOR]

In this paper, we propose a methodology to evaluate the effects on the value-added estimators when there is a measurement error in variables. These errors lead to biased estimators; in most cases, these estimators have a large variance. The proposed methodology is illustrated under two scenarios: First, simulation studies for discussing the effects on value-added estimates for some values of reliability. Second, an application for real data using a Colombian database that contains the scores of two standardized tests: Saber 11 and Saber Pro. In the first test, the students are evaluated in the 11th grade of high school. In the second test, the students are evaluated in the last two years of the undergraduate program. We also implement the estimation method taking into account measurement error and adapt the Bootstrap procedure to Hierarchical Linear Mixed Model with error in variables. The results show that measurement error affects the higher-education value-added and the estimator variance. This implies universities can be incorrectly classified. Therefore, we may assert that any given university is contributing to students' progress when, in reality, this is not so. [ABSTRACT FROM AUTHOR]

The multinomial distribution is often used in modeling categorical data because it describes the probability of a random observation being assigned to one of several mutually exclusive categories. Given a finite or numerable multinomial model M | n , p whose decision is indexed by a parameter θ and having a cost c θ , p depending on θ and on p , we show that, under general conditions, the probability of taking the least cost decision tends to 1 when n tends to ∞, i.e., we showed that the cost decision is consistent, representing a Statistical Decision Theory approach to the concept of consistency, which is not much considered in the literature. Thus, under these conditions, we have consistency in the decision making. The key result is that the estimator p ˜ n with components p ˜ n , i = n i n , i = 1 , ⋯ , where n i is the number of times we obtain the ith result when we have a sample of size n, is a consistent estimator of p. This result holds both for finite and numerable models. By this result, we were able to incorporate a more general form for consistency for the cost function of a multinomial model. [ABSTRACT FROM AUTHOR]

Most reported treatment effects in medical research studies are ambiguously defined, which can lead to misinterpretation of study results. This is because most authors do not attempt to describe what the treatment effect represents, and instead require readers to deduce this based on the reported statistical methods. However, this approach is challenging, because many methods provide counterintuitive results. For example, some methods include data from all patients, yet the resulting treatment effect applies only to a subset of patients, whereas other methods will exclude certain patients while results will apply to everyone. Additionally, some analyses provide estimates pertaining to hypothetical settings in which patients never die or discontinue treatment. Herein we introduce estimands as a solution to the aforementioned problem. An estimand is a clear description of what the treatment effect represents, thus saving readers the necessity of trying to infer this from study methods and potentially getting it wrong. We provide examples of how estimands can remove ambiguity from reported treatment effects and describe their current use in practice. The crux of our argument is that readers should not have to infer what investigators are estimating; they should be told explicitly. [ABSTRACT FROM AUTHOR]

We propose a novel family of estimators for the population mean under non-response and obtain the MSE equation of the suggested estimator for each situation in theory. These theoretical conditions are applied to three popular data sets in literature and we see that the suggested estimators are more efficient than the traditional estimators, such as ratio, regression estimators, in Case 1; whereas, in Case 2, the suggested estimators are also more efficient than the Unal-Kadilar exponential estimators that are more efficient than the traditional estimators for the same data sets. [ABSTRACT FROM AUTHOR]

We display the power Topp-Leone (PTL) distribution with two parameters. The following major features of the PTL distribution are investigated: quantile measurements, certain moment's measures, residual life function, and entropy measure. Maximum likelihood, least squares, Cramer von Mises, and weighted least squares approaches are used to estimate the PTL parameters. A numerical illustration is prepared to compare the behavior of the achieved estimates. Data analysis is provided to scrutinize the flexibility of the PTL model matched with Topp-Leone distribution. [ABSTRACT FROM AUTHOR]

Phenotypic plasticity index (PI), the slope of reaction norm (K) and relative distances plasticity index (RDPI), the most commonly used estimators, have occasionally been found to generate different plasticity rankings between groups (species, populations, cultivars or genotypes). However, no effort has been made to determine how frequent this incongruence is, and the factors that influence the occurrence of the incongruence. To address these problems, we first proposed a conceptual framework and then tested the framework (its predictions) by reanalyzing 1248 sets of published data. Our framework reveals inherent conflicts between K and PI or RDPI when comparing plasticity between two groups, and the frequency of these conflicts increases with increasing inter-group initial trait difference and/or K values of the groups compared. More importantly, the estimators also affect the magnitude of the inter-group plasticity differences even when they do not change groups' plasticity rankings. The above-mentioned effects of plasticity estimators were confirmed by our empirical test using data from the literature, and the conflicts occur in 203 (16%) of the 1248 comparisons between K and indices, indicating that a considerable proportion of the comparative conclusions on plasticity in literature are estimator-dependent. The frequency of the conflicts is influenced by phylogenetic relatedness of the groups compared, being lower when comparing within relative to between species, but not by specific types of environments, traits and species. Our study indicates that care is needed to select estimator when comparing groups' plasticity, and that the conclusions in relevant literature should be treated with great caution. [ABSTRACT FROM AUTHOR]

The life risk factor for the cancer disease is becoming more and more in the present living life style. Many researchers focused on skin cancer using machine learning approaches, but when working with multi classification data, it is observed that ANN gives better results, if the input data is keeps on adding in the real time scenario. This proposed system focuses on the classification of the skin cancer types by designing the neural network based on the customized activation function. Many parameters are involved in designing a suitable classifier using NN like learning rate, optimizer, activator, and other normalization layers. This paper majorly focuses on the activation function and number of neurons associated with the layer because these two parameters play a vital role in the entire accuracy of the model. Among the existing activators, the combination of tanh and relu has given high value base on them; a new activation function is designed. [ABSTRACT FROM AUTHOR]

We use simple examples to show how the bias and standard error of an estimator depend in part on the type of estimator chosen from among parametric, nonparametric, and semiparametric candidates. We estimated the cumulative distribution function in the presence of missing data with and without an auxiliary variable. Simulation results mirrored theoretical expectations about the bias and precision of candidate estimators. Specifically, parametric maximum likelihood estimators performed best but must be "omnisciently" correctly specified. An augmented inverse probability–weighted (IPW) semiparametric estimator performed best among candidate estimators that were not omnisciently correct. In one setting, the augmented IPW estimator reduced the standard error by nearly 30%, compared with a standard Horvitz-Thompson IPW estimator; such a standard error reduction is equivalent to doubling the sample size. These results highlight the gains and losses that can be incurred when model assumptions are made in any analysis. [ABSTRACT FROM AUTHOR]

The study covered four (4) selected financial deepening estimators as it affects the economy as a whole. To carry out the study, data were sourced through Statistical Bulletin 2018 and World Bank Development Indicators (2018) from 2003 to 2018. Group of low-income countries have continued to find the need for adequate financial deepening in a bid to enhance their economic growth. Though they are operating a modern type of economy, yet they are mostly mono-economy, relying more on either oil or agricultural products and heavily depending on oil. Hence, Nigeria is a small open economy. In analyzing the data obtained, linear regression analysis was used through SPSS 22.0. The study formulated four (4) hypotheses and the findings showed that the ratio of money supply, credit ratio in private sector, savings ratio and investment have impact significantly on Gross Domestic Product in Nigeria because the p-value t-statistics are 0.0481, 0.027, 0.046 and.000 respectively are all less than 5% significant level. The study, therefore, identified the nexus by concluding that financial deepening has significant impact on Nigeria's economic growth. This study recommends that government policies should be geared towards increased money supply and well-organized capital market that can improve general economic efficacy. [ABSTRACT FROM AUTHOR]

The paper addresses the problem of constructing lower and upper estimators for univariate functions. This problem is of crucial importance in global optimization, where such bounds are used to reduce the search area. We propose to use piecewise linear estimators for bounding univariate functions and show how such estimators can be derived from the function's algebraic expression. The basic properties of such estimators are formulated and proved. We implemented the algorithms for the automated construction of lower and upper piecewise linear estimators and experimentally compared the proposed approach with the first-order interval bounds, Pijavskij method, and slope arithmetic. Numerical examples demonstrate that the piecewise linear estimators are more accurate with respect to the mentioned approaches. We also show that global optimization algorithms can significantly benefit from using piecewise linear estimators. Another advantage of the proposed approach is that the objective function does not have to be differentiable. This feature can favorably distinguish this method from other methods where the first and second derivatives are used. [ABSTRACT FROM AUTHOR]

Completeness of BMS (Butterfly Monitoring Scheme) butterfly censuses in Spain has been calculated from a total of 86 transects distributed in 21 Spanish provinces. The number of species for each transect was estimated using both parametric (rational function) and non-parametric estimators (Chao 1 and 2, Jackknife 1 and 2, Bootstrap, ICE, ACE). Results show that 75% of the transects have a completeness greater than 70%, and there are no significant differences between estimators. Correlation of completeness with other variables was also studied, the most related being number of visits, number of individuals and number of species of each transect. Finally, geographical distribution of richness and completeness was analysed and only richness resulted to be correlated with longitude and latitude, which discards the idea of a geographic bias in completeness. [ABSTRACT FROM AUTHOR]

In this article, we have highlighted limitations of existing structures of X ¯ control chart for unknown parameters by considering various circumstances of a process. The circumstances include availability of limited samples for estimating control limits, probability distribution is unknown and collected data are highly skewed. To tackle with the limitations, we have proposed generalized skewness correction structure of X ¯ chart. For proposing the required structure, we have developed skewness correction based dispersion estimators and corrected control limits multipliers to replace with known probability distribution based dispersion estimator and control limits multipliers. The proposed generalized skewness structure is dependent on the amount of skewness of gathered data from an ongoing process instead of restricted assumptions. Results illustrate that actual false alarm rate of proposed structure remains close to true false alarm rate as compared to existing structures when assumptions are violated. Besides, a real-life example from petrochemical process is presented for explaining the implementation procedure of proposed structure. [ABSTRACT FROM AUTHOR]

Studies included in meta-analysis can produce results that depart from the true population parameter of interest due to systematic and/or random errors. Synthesis of these results in meta-analysis aims to generate an estimate closer to the true population parameter by minimizing these errors across studies. The inverse variance heterogeneity (IVhet), quality effects and random effects models of meta-analysis all attempt to do this, but there remains controversy around the estimator that best achieves this goal of reducing error. In an attempt to answer this question, a simulation study was conducted to compare estimator performance. Five thousand iterations at 10 different levels of heterogeneity were run, with each iteration generating one meta-analysis. The results demonstrate that the IVhet and quality effects estimators, though biased, have the lowest mean squared error. These estimators also achieved a coverage probability at or above the nominal level (95%), whereas the coverage probability under the random effects estimator significantly declined (<80%) as heterogeneity increased despite a similar confidence interval width. Based on our findings, we would recommend the use of the IVhet and quality effects models and a discontinuation of traditional random effects models currently in use for meta-analysis. [ABSTRACT FROM AUTHOR]

In this study, we have considered two design structures of X¯ control chart by covering the situations of known and unknown parameters, variety of probability distributions, and runs rules. The design structures are dependent on constants which generally considered hard to compute analytically. For construction of constants and also for evaluating performance of the design structures through performance measures, we have illustrated Monte Carlo simulation procedure/algorithm for researcher and practitioners. Furthermore, based on the Monte Carlo simulation procedures, we have established a program in R language to compute values of different constants and performance measures. Results illustrated that design structures for known and unknown parameters under variety of runs rules and probability distributions have outstanding performance in contrast to existing structures. Moreover, design structure for unknown parameters behaves alike the design structure for known parameters. This indicates that design structure for unknown parameters has the ability to resolve the issue of runs rules which generally occur when parameters are estimated. Besides, two real‐life examples have been included in which physicochemical characteristic of groundwater and plasticizer characteristic of petrochemical process are monitored through design structures. [ABSTRACT FROM AUTHOR]

In this article, we propose new estimators of location. These estimators select a robust set around the geometric median, enlarge it, and compute the (iterative) weighted mean from it. By doing so, we obtain a robust estimator in the sense of the breakdown point, which uses more observations than standard estimators. We apply our approach on the concepts of boxplot and bagplot. We work in a general normed vector space and allow multi-valued estimators. [ABSTRACT FROM AUTHOR]

A selective review of the most recent developments in the estimation of the covariance matrix with a number of samples smaller than the ambient dimension is presented. In particular, estimators are considered for low rank and sparse structures, paying special attention to the algorithms most used in practice. Estimators that resort to the use of classical techniques such as thresholds and singular-value decomposition (SVD), as well as newest estimators based on random matrices and convex optimization are presented. One of the main conclusions of the study is that, in spite of the great advances in the development of new estimators, issues related to the computation time of the convex programs that use the estimators have been little explored. Additionally, lack of works on the development of estimators for matrices with jointly sparse and low rank structure is observed [ABSTRACT FROM AUTHOR]

A novel approach to onboard in-flight weight and balance estimation systems is presented. Data from an Airbus A320 fleet from an airline were used to assess the feasibility of the approach. Simple flight mechanics in combination with statistics allowed for the identification of weight and center of gravity position using cruise angle of attack, Mach number and elevator deflection values. The good agreement between the theoretical model and the obtained values for the lift curve slope as a function of Mach as well as the standard error of the estimate for center of gravity position and cruise flying weight indicate that the method is sound. The major implication of this work is that the development of onboard and in-flight weight and balance systems can be significantly simpler than previous literature suggested. The impact of this paper could be immediate for airlines since all the tools required to implement the system as described are readily available. This could have an effect in operating costs, safety and environment. [ABSTRACT FROM AUTHOR]

The degree of supersaturation of the mother liquor is a key factor in improving the monitoring and control of the final stage of industrial sugar crystallization. However, the difficulty of obtaining online supersaturation measurements is one of the challenges associated with monitoring and controlling sugar crystallization. There is no direct method or single instrument for measuring supersaturation. It can only be calculated or inferred from other measurements. In the literature, estimators of mother liquor supersaturation are reported, typically focused on the first stage of crystallization. The SeedMaster series transmitters are the sole industrial instruments that provide online supersaturation information by calculating it from external measurements. The purpose of this study is to design a first-principles model-based soft-sensor as a practical alternative to obtain real-time information about supersaturation in the last stage of sugar crystallization. The proposed estimator relies on two models: a supersaturation model and a second simplified model of the last stage of crystallization. The parameters of both models were estimated based on real industrial data. The estimation is performed in three steps: 1. An Unscented Kalman Filter estimates the states of the crystallization model and their variance. 2. The estimated supersaturation value is obtained by substituting the estimated states into the supersaturation model. 3. The estimator's bias, and variance are calculated to establish error bounds. The main characteristics of the obtained estimator are: practical unbiasedness, nearly minimum variance and robustness. The performance and behavior of the supersaturation estimator are contrasted using real data from an industrial crystallization plant (Urbano Noris factory, Holguín, Cuba). Regardless of its initial conditions, the estimator converges to the three standard deviation error band in less than three minutes. The exact time may vary depending on how much the estimator's initial conditions deviate from those of the process. After this time (Reach Time), the estimates remain within the calculated error limits of three standard deviations. The maximum absolute errors obtained were less than 0.019 units, corresponding to a maximum relative error of less than 1.5%. These values are favorable since they are well below critical values (0.125 units of absolute error). Moreover, the error bands are much smaller than the operating zone width (approximately 0.25 units), which is a necessary condition for any supersaturation estimator to be useful. Finally, it should be noted that the errors have been reduced compared to the values reported in previous research focused on the sugar industry using other techniques. • Supersaturation estimator for monitoring the industrial sugar crystallization process. • A simple and flexible design with practical unbiasedness and nearly minimum variance. • Supersaturation is estimated from level, brix, and temperature measurements. • The estimator was validated with real data from an industrial sugar plant. • The small errors obtained guarantee the reliability and accuracy of the estimator. [ABSTRACT FROM AUTHOR]

In this work, we study the accuracy and efficiency of hierarchical matrix ( H -matrix) based fast methods for solving dense linear systems arising from the discretization of the 3D elastodynamic Green's tensors. It is well known in the literature that standard H -matrix based methods, although very efficient tools for asymptotically smooth kernels, are not optimal for oscillatory kernels. H 2 -matrix and directional approaches have been proposed to overcome this problem. However the implementation of such methods is much more involved than the standard H -matrix representation. The central questions we address are twofold. (i) What is the frequency-range in which the H -matrix format is an efficient representation for 3D elastodynamic problems? (ii) What can be expected of such an approach to model problems in mechanical engineering? We show that even though the method is not optimal (in the sense that more involved representations can lead to faster algorithms) an efficient solver can be easily developed. The capabilities of the method are illustrated on numerical examples using the Boundary Element Method. [ABSTRACT FROM AUTHOR]

Health care professionals often use regression methods to quantitatively describe the functional relationships between predictors and outcomes. However, little research investigates the appropriateness of tool application for response-surface-based design of experiments, the choice of regression estimators under different environments, and the impact of the determination of optimum conditions, or optimal factor-level settings, to achieve desirable target outcomes. Robust parameter design (RPD) is an established methodology for determining optimum conditions for a process to achieve specified process targets while minimizing variability of the outcomes. Underlying assumptions for RPD modeling and process conditions should be taken into account when selecting a regression estimator for developing fitted models. If these assumptions are incorrect, then a direct use of estimates obtained has the potential to be problematic, and the results may be potentially catastrophic, particularly when applied to the health care field. Many approaches to RPD in existing literature use ordinary least squares to obtain response functions by assuming normality and moderate variability in the underlying process. Given that some biologic processes are often highly variable and inherently asymmetric, a conditions-based approach for the selection of a regression estimate technique needs to be explored. This paper examines alternative approaches to regression estimation when the process data indicates that asymmetry or a high degree of process variability exists. The performance of select alternative regression methods is compared using Monte Carlo simulation and numerical analysis. [ABSTRACT FROM AUTHOR]

The concept of species richness is currently the most basic and most often used parameter in quantitative assessments of biodiversity. The species richness of Lumbricidae was investigated in East Serbia, one of the highly complex areas in the eastern and central part of the Balkan Peninsula. Our database included 2615 individuals from a total of 54 species. Quantification of species richness was done by using the observed number of species and richness estimators. A nonparametric approach was used to evaluate the performance of various estimation techniques: Chao 2, Jackknife 2, and Bootstrap. However, only Chao 2 reached the asymptote, maintaining values of 77 until the end of the curve. A total of 70.12% of the estimated number of Lumbricidae living in East Serbia were detected in our field study and we should expect that 23 lumbricid taxa will be added to the inventory in the future. The sampling effort needed to find additional species undetected during the sampling is 214 individuals. Chao 2 generally outperforms other estimators since it attains near-asymptotic stability and, therefore, more accurate prediction of earthworm species richness. [ABSTRACT FROM AUTHOR]

In this study, the stand volume (V, m³ ha-1) of Pinus radiata plantations of the province of Lugo (NW of Spain) was estimated using LiDAR-derived metrics from the low-density countrywide LiDAR dataset provided by the Spanish National Aerial Photography Program. The estimate was based on a oriented field inventory design to improve the fit of the model and on a inferential method based on the variability of the model parameter estimation and the variability of the LiDAR variable in the target population, which allowed to estimate the population mean and the standard error of V. A total of 25 permanent field inventory plots --that represent adequately the existing range of ages, stand densities and sites of the species in the province-- were measured in order to fit the empirical relationship between field-measured V and LiDAR-derived metrics, using linear and (multiplicative) power function models. Regression analysis indicated a strong relationship between V and the 70 percentile of the LiDAR normalized height distribution (h70) using a power function model (R² = 0.899; RMSE = 63.4 m³ ha-1). The selected model was applied to all timber and high-pole development stands of P. radiata with coverage of this species superior to the 60 % according to the Spanish National Forest Map. Finally, a spatial-explicit map that describes V was produced. In 2009, the estimated mean population for V was 166.3 m³ ha-1 with a relative sampling error of 13.9 %. This methodology will allow generating thematic maps every six years (temporal resolution scheduled for the PNOA LiDAR flights). [ABSTRACT FROM AUTHOR]

Pholcidae is one of the species-rich spider families. However, pholcid spiders are poorly studied in Southeast Asian countries like the Philippines. In this study, selected areas of southeastern Mindanao, Philippines were surveyed in order to determine the pholcid fauna in the area. Pholcids were collected using a combination of aerial-hand searching and ground-hand searching methods. Nonparametric richness estimators were used to estimate the species richness of the sampling area using EstimateS version 9.0 software. In addition, biodiversity and similarity indices were calculated using Paleontological Statistics Software Package (PAST) version 2.17c. Seventeen species of pholcid spiders were documented in which 88% of the samples collected were probably new species. Based on richness estimates computed, the true species richness in the surveyed areas might exceed 37 species and probably much more. The highest species diversity among the six sampling areas was documented in Mount Hamiguitan Wildlife Sanctuary located at San Isidro, Davao Oriental. However, Epol falls in Marilog District seems to be the distinct sampling area among the six sites surveyed because of the unique species found in the area. The results of this study clearly showed that the diversity of pholcid spiders is much higher in the country than the 13 species that were previously documented. [ABSTRACT FROM AUTHOR]

During the last decade, variance control charts based on different sampling schemes have attracted research interest in the field of statistical process control. These charts used extra (auxiliary) information either for ranking of units or estimation rather than using it for both. The effectiveness of a control chart can be increased by utilizing the auxiliary information for dual purposes. This article is focused on developing a generalized structure of variance control charts based on dual use of auxiliary information under different sampling strategies and runs rules. The generalized structure mainly depends on three auxiliary information-based estimators with dual use of auxiliary information, three bivariate process distributions, and variety of sampling schemes. The performance of the proposed control charts is investigated by assessing the power curve. We have observed that the proposals of the study perform better than its complement. An application example is also provided for practitioners' concerns to monitor the stability of physicochemical parameter of groundwater. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The net survival of a patient diagnosed with a given disease is a quantity often interpreted as the hypothetical survival probability in the absence of causes of death other than the disease. In a relative survival framework, net survival summarises the excess mortality that patients experience compared with their relevant reference population. Based on follow-up data from the Finnish Cancer Registry, we derived simulation scenarios that describe survival of patients in eight cancer sites reflecting different excess mortality patterns in order to compare the performance of the classical Ederer II estimator and the new estimator proposed by Pohar Perme et al. At 5 years, the age-standardised Ederer II estimator performed equally well as the Pohar Perme estimator with the exception of melanoma in which the Pohar Perme estimator had a smaller mean squared error (MSE). At 10 and 15 years, the age-standardised Ederer II performed most often better than the Pohar Perme estimator. The unstandardised Ederer II estimator had the largest MSE at 5 years. However, its MSE was often superior to those of the other estimators at 10 and 15 years, especially in sparse data. Both the Pohar Perme and the age-standardised Ederer II estimator are valid for 5-year net survival of cancer patients. For longer-term net survival, our simulation results support the use of the age-standardised Ederer II estimator. [ABSTRACT FROM AUTHOR]

An approach to interval observer design for Linear Parameter-Varying (LPV) systems is proposed. It is assumed that the vector of scheduling parameters in LPV models is not available for measurement. Two different interval observers are constructed for nonnegative systems and for a generic case. Stability conditions are expressed in terms of matrix inequalities, which can be solved with respect to the observer gains using standard numerical solvers. Applying L 1 / L 2 framework the robustness and estimation accuracy with respect to model uncertainty are analyzed. The efficiency of interval estimation for LPV models is demonstrated through numerical experiments for a microfluidic system and an academic example. [ABSTRACT FROM AUTHOR]

Genetic marker-based estimators remain a popular tool for measuring relatedness ( r xy) and inbreeding ( F) coefficients at both the population and individual level. The performance of these estimators fluctuates with the number and variability of markers available, and the relatedness composition and demographic history of a population. Several methods are available to evaluate the reliability of the estimates of r xy and F, some of which are implemented in the program COANCESTRY. I used the simulation module in COANCESTRY since assess the performance of marker-based estimators of r xy and F in a species with very low genetic diversity, New Zealand's little spotted kiwi ( Apteryx owenii). I also conducted a review of published papers that have used COANCESTRY as its release to assess whether and how the reliability of the estimates of r xy and F produced by genetic markers are being measured and reported in published studies. My simulation results show that even when the correlation between true (simulated) and estimated r xy or F is relatively high (Pearson's r = 0.66-0.72 and 0.81-0.85, respectively) the imprecision of the estimates renders them highly unreliable on an individual basis. The literature review demonstrates that the majority of studies do not report the reliability of marker-based estimates of r xy and F. There is currently no standard practice for selecting the best estimator for a given data set or reporting an estimator's performance. This could lead to experimental results being interpreted out of context and render the robustness of conclusions based on measures of r xy and F debatable. [ABSTRACT FROM AUTHOR]

We present a general analytic technique for computing moments of estimators of parameters of an AR(k) model. These moments can then be utilized to construct aaccurate PDF of the corresponding sampling distribution. [ABSTRACT FROM AUTHOR]

This paper considers the problem of estimating the population proportion of a categorical variable using the calibration framework. Different situations are explored according to the level of auxiliary information avail- able and the theoretical properties are investigated. A new class of estimator based upon the proposed calibration estimators is also defined, and the optimal estimator in the class, in the sense of minimal variance, is derived. Finally, an estimator of the population proportion, under new calibration conditions, is defined. Simulation studies are considered to evaluate the performance of the proposed calibration estimators via the empirical relative bias and the empirical relative efficiency, and favourable results are achieved. [ABSTRACT FROM AUTHOR]

In this paper, we focus on Pitman closeness probabilities when the estimators are symmetrically distributed about the unknown parameter θ. We first consider two symmetric estimators θˆ1and θˆ2and obtain necessary and sufficient conditions for θˆ1to be Pitman closer to the common median θ than θˆ2. We then establish some properties in the context of estimation under the Pitman closeness criterion. We define Pitman closeness probability which measures the frequency with which an individual order statistic is Pitman closer to θ than some symmetric estimator. We show that, for symmetric populations, the sample median is Pitman closer to the population median than any other independent and symmetrically distributed estimator of θ. Finally, we discuss the use of Pitman closeness probabilities in the determination of an optimal ranked set sampling scheme (denoted by RSS) for the estimation of the population median when the underlying distribution is symmetric. We show that the best RSS scheme from symmetric populations in the sense of Pitman closeness is the median and randomized median RSS for the cases of odd and even sample sizes, respectively. [ABSTRACT FROM PUBLISHER]

Structural equation models with interaction and quadratic effects have become a standard tool for testing nonlinear hypotheses in the social sciences. Most of the current approaches assume normally distributed latent predictor variables. In this article, we describe a nonlinear structural equation mixture approach that integrates the strength of parametric approaches (specification of the nonlinear functional relationship) and the flexibility of semiparametric structural equation mixture approaches for approximating the nonnormality of latent predictor variables. In a comparative simulation study, the advantages of the proposed mixture procedure over contemporary approaches [Latent Moderated Structural Equations approach (LMS) and the extended unconstrained approach] are shown for varying degrees of skewness of the latent predictor variables. Whereas the conventional approaches show either biased parameter estimates or standard errors of the nonlinear effects, the proposed mixture approach provides unbiased estimates and standard errors. We present an empirical example from educational research. Guidelines for applications of the approaches and limitations are discussed. [ABSTRACT FROM PUBLISHER]

In the past decade new approaches for the estimation of latent nonlinear interaction and quadratic effects in structural equation modeling have been proposed (Kelava & Brandt, 2009; Klein & Moosbrugger, 2000; Klein & Muthén, 2007; Marsh, Wen, & Hau, 2004; Mooijaart & Bentler, 2010; Wall & Amemiya, 2003). Most approaches have been developed for the analysis of normally distributed latent predictor variables. In this article, we investigate the performance of five recent approaches under the condition of nonnormally distributed data: the extended unconstrained approach (Kelava & Brandt, 2009), LMS (Klein & Moosbrugger, 2000), QML (Klein & Muthén, 2007), the 2SMM approach (Wall & Amemiya, 2003), and the method of moments approach by Mooijaart and Bentler (2010). Advantages and limitations of the approaches are discussed. [ABSTRACT FROM AUTHOR]

A regression methodology is introduced that obtains competitive, robust, efficient, high-breakdown regression parameter estimates as well as providing an informative summary regarding possible multiple outlier structure. The proposed method blends a cluster analysis phase with a controlled bounded influence (BI) regression phase, thereby referred to as cluster-based bounded influence regression, or CBI. Representing the data space via a special set of anchor points, a collection of point-addition OLS regression estimators forms the basis of a metric used in defining the similarity between any two observations. Cluster analysis then yields a main cluster 'half-set' of observations, with the remaining observations comprising one or more minor clusters. An initial regression estimator arises from the main cluster, with a group-additive DFFITS argument used to carefully activate the minor clusters through a BI regression frame work. CBI achieves a 50% breakdown point, is regression equivariant, scale and affine equivariant and distributionally is asymptotically normal. Case studies and Monte Carlo results demonstrate the performance advantage of CBI over other popular robust regression procedures regarding coefficient stability, scale estimation and standard errors. The dendrogram of the clustering process and the weight plot are graphical displays available for multivariate outlier detection. Overall, the proposed methodology represents advancement in the field of robust regression, offering a distinct philosophical view point towards data analysis and the marriage of estimation with diagnostic summary. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We present a theory of bridge homogeneous volatility estimators for log-price stochastic processes. Starting with the standard definition of a Brownian bridge as the conditional Wiener process with two endpoints fixed, we introduce the concept of an incomplete bridge by breaking the symmetry between the two endpoints. For any given time interval, this allows us to encode the information contained in the open, high, low and close prices into an incomplete bridge. The efficiency of the new proposed estimators is favourably compared with that of the classical Garman–Klass and Parkinson estimators. [ABSTRACT FROM PUBLISHER]

Abstract: The focus of this paper is the analysis of a high-gain observer for estimating un-steady inputs, including guarantees for: (1) robustness to measurement uncertainty, and (2) transient upper bound on estimator error. A method for selecting the observer gain based on measurement uncertainty, acceptable steady-state errors, and desired estimation error convergence rates, is also described. This strategy is demonstrated in practice for internal combustion (IC) engine effective compression ratio (ECR) estimation. Experimental results are shown to be consistent with analytical guarantees—convergence within 4 engine cycles, and a steady-state error less than 0.5 ECR, in the presence of 10% measurement error. [Copyright &y& Elsevier]

Determination of mechanical properties of materials requires repeatable and consistent results. Therefore, the Weibull statistical distribution is widely used to verify the confidence levels of the test results where probability of failure is predicted. The reliability of results is characterised by Weibull distribution where Weibull parameters are determined and compared with the benchmarks. Recent studies showed that the size of the population and the method chosen to estimate the Weibull modulus play an important role. Therefore, in this study, the effect of ranking selection over the Weibull parameters (alpha-characteristic life, beta-shape parameter, R² and survival probability) was investigated. The data from authors' recent research were re-evaluated. In addition, randomly generated data with population from 5 to 50 sample sizes were studied. The results showed that the mean rank regression to estimate the failure probability had the highest R² and the lowest shape parameter. It was also found that the results were independent of sample size. [ABSTRACT FROM AUTHOR]

Abstract: The ability to perform online model identification for nonlinear systems with unknown dynamics is essential to any adaptive model-based control system. In this paper, a new differential equality constrained recursive least squares estimator for multivariate simplex splines is presented that is able to perform online model identification and bounded model extrapolation in the framework of a model-based control system. A new type of linear constraints, the differential constraints, are used as differential boundary conditions within the recursive estimator which limit polynomial divergence when extrapolating data. The differential constraints are derived with a new, one-step matrix form of the de Casteljau algorithm, which reduces their formulation into a single matrix multiplication. The recursive estimator is demonstrated on a bivariate dataset, where it is shown to provide a speedup of two orders of magnitude over an ordinary least squares batch method. Additionally, it is demonstrated that inclusion of differential constraints in the least squares optimization scheme can prevent polynomial divergence close to edges of the model domain where local data coverage may be insufficient, a situation often encountered with global recursive data approximation. [Copyright &y& Elsevier]

Interaction and quadratic effects in latent variable models have to date only rarely been tested in practice. Traditional product indicator approaches need to create product indicators (e.g., x12, x1 x4) to serve as indicators of each nonlinear latent construct. These approaches require the use of complex nonlinear constraints and additional model specifications and do not directly address the nonnormal distribution of the product terms. In contrast, recently developed, easy-to-use distribution analytic approaches do not use product indicators, but rather directly model the nonlinear multivariate distribution of the measured indicators. This article outlines the theoretical properties of the distribution analytic Latent Moderated Structural Equations (LMS; Klein & Moosbrugger, 2000) and Quasi-Maximum Likelihood (QML; Klein & Muthen, 2007) estimators. It compares the properties of LMS and QML to those of the product indicator approaches. A small simulation study compares the two approaches and illustrates the advantages of the distribution analytic approaches as multicollinearity increases, particularly in complex models with multiple nonlinear terms. An empirical example from the field of work stress applies LMS and QML to a model with an interaction and 2 quadratic effects. Example syntax for the analyses with both approaches is provided. [ABSTRACT FROM AUTHOR]

Abstract: There has been a growing interest in the estimation of information carried by a single neuron and multiple single units or population of neurons to specific stimuli. In this paper we analyze, inspired by article of , the efficiency of a neuronal communication by considering dendrosomatic summation as a and by considering such uncertain synaptic transmission as part of the dendrosomatic computation. Specifically, we study Mutual Information between input and output signals for different types of neuronal network architectures by applying efficient entropy estimators. We analyze the influence of the following quantities affecting transmission abilities of neurons: synaptic failure, activation threshold, firing rate and type of the input source. We observed a number of surprising non-intuitive effects. It turns out that, especially for lower activation thresholds, significant synaptic noise can lead even to twofold increase of the transmission efficiency. Moreover, the efficiency turns out to be a non-monotonic function of the activation threshold. We find a universal value of threshold for which a local maximum of Mutual Information is achieved for most of the neuronal architectures, regardless of the type of the source (correlated and non-correlated). Additionally, to reach the global maximum the optimal firing rates must increase with the threshold. This effect is particularly visible for lower firing rates. For higher firing rates the influence of synaptic noise on the transmission efficiency is more advantageous. Noise is an inherent component of communication in biological systems, hence, based on our analysis, we conjecture that the neuronal architecture was adjusted to make more effective use of this attribute. [Copyright &y& Elsevier]

Abstract: Three different estimators are presented for the types of parameters present in mathematical models of animal epidemics. The estimators make use of the data collected during an epidemic, which may be limited, incomplete, or under collection on an ongoing basis. When data are being collected on an ongoing basis, the estimated parameters can be used to evaluate putative control strategies. These estimators were tested using simulated epidemics based on a spatial, discrete-time, gravity-type, stochastic mathematical model containing two parameters. Target epidemics were simulated with the model and the three estimators were implemented using various combinations of collected data to independently determine the two parameters. [Copyright &y& Elsevier]

Abstract: Many deep web data sources are ranked data sources, i.e., they rank the matched documents and return at most the top k number of results even though there are more than k documents matching the query. While estimating the size of such ranked deep web data source, it is well known that there is a ranking bias—the traditional methods tend to underestimate the size when queries overflow (match more documents than the return limit). Numerous estimation methods have been proposed to overcome the ranking bias, such as by avoiding overflowing queries during the sampling process, or by adjusting the initial estimation using a fixed function. We observe that the overflow rate has a direct impact on the accuracy of the estimation. Under certain conditions, the actual size is close to the estimation obtained by unranked model multiplied by the overflow rate. Based on this result, this paper proposes a method that allows overflowing queries in the sampling process. [Copyright &y& Elsevier]

This paper addresses the problem of estimating the size of a deep web data source that is accessible by queries only. Since most deep web data sources are non-cooperative, a data source size can only be estimated by sending queries and analyzing the returning results. We propose an efficient estimator based on the capture–recapture method. First we derive an equation between the overlapping rate and the percentage of the data examined when random samples are retrieved from a uniform distribution. This equation is conceptually simple and leads to the derivation of an estimator for samples obtained by random queries. Since random queries do not produce random documents, it is well known that the traditional methods by random queries underestimate the size, i.e., those estimators have negative bias. Based on the simple estimator for random samples, we adjust the equation so that it can handle the samples returned by random queries. We conduct both simulation studies and experiments on corpora including Gov2, Reuters, Newsgroups, and Wikipedia. The results show that our method has small bias and standard deviation. [ABSTRACT FROM AUTHOR]

Abstract: A new methodology for creating highly accurate, static nonlinear maps from scattered, multivariate data is presented. This new methodology uses the B-form polynomials of multivariate simplex splines in a new linear regression scheme. This allows the use of standard parameter estimation techniques for estimating the B-coefficients of the multivariate simplex splines. We present a generalized least squares estimator for the B-coefficients, and show how the estimated B-coefficient variances lead to a new model quality assessment measure in the form of the B-coefficient variance surface. The new modeling methodology is demonstrated on a nonlinear scattered bivariate dataset. [Copyright &y& Elsevier]

Abstract: There is strong evidence that low frequency between-year environmental variability, in addition to fishing, is able to affect fish population abundance via recruitment. However, scientific advice regarding catch limits is often based on control rules that depend on the estimation of biomass reference points which typically do not explicitly consider the effects of trends over time in reference points caused by environmental variability. Harvest rates based on commonly used biological reference points such as the level of unfished spawning biomass (B 0), the current size of the stock in relation to B 0, and B MSY that are sustainable under current environmental conditions may be unsustainable under different environmental conditions. Although several methods exist for estimating biomass reference points, it is unclear which of these are most robust to the effects of long term, low frequency environmental variability. Therefore, simulation is used to evaluate alternative estimators, which differ in terms of how the stock–recruitment relationship is modeled, and whether explicit estimators or proxies are used for B 0, the steepness of the stock–recruitment relationship, and current spawning biomass relative to B 0. The simulations consider three life histories: a long-lived unproductive rockfish, a moderately long-lived and productive flatfish, and a moderately long-lived and productive hake with highly variable recruitment. Results indicate that in the presence of low frequency autocorrelated forcing of recruitment, biomass reference points should be based on average recruitment and/or dynamic B 0 if catch and survey data are available for at least one full period of the environmental variable. In contrast, previous analysis suggests that in the absence of autocorrelated environmental forcing of recruitment, and if the available catch and survey data do not span at least, in this case, 50 years which is one full period of the environmental variable, biomass reference points should be based on the fit of the stock–recruitment relationship. Life history affects the estimability of biomass reference points, which are more difficult to estimate for species with more rapid dynamics such as hake. The method used to calculate the reference points given the results of a stock assessment has a larger effect on estimability than the configuration of the stock assessment method, for the three stock assessment model configurations investigated in this study. [Copyright &y& Elsevier]

Abstract: Recent analyses on some daily rainfall time series highlighted the presence of records with anomalous rounding (1 and 5mm), while the standard resolution should be 0.1 or 0.2mm. Assuming that the generalized Pareto distribution (GPD) can reliably represent the distribution of daily rainfall depths, this study investigates how such discretizations can affect the inference process. The performances of several GPD estimators are compared using the Monte Carlo approach. Synthetic samples are drawn by GPDs with shape and scale parameters in the ranges of values estimated on daily rainfall depth time series. Results show how the relative efficiency of estimators could be very different for continuous or rounded-off samples. Moreover, when the rounding-off magnitude becomes larger than a few millimeters, all the considered estimators reveal very poor performances. [Copyright &y& Elsevier]