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Given the need to make efficient use of the resources allocated to experimental research in sugarcane crops, this research was proposed with the aim of determining the optimal size of the experimental plot in sugarcane crops in the Brunca region of Costa Rica. During the 2018-2019 harvest, an experimental uniformity trial was established, and five methods were compared: maximum curvature method, maximum curvature of the coefficient of variation, linear regression with constant, quadratic regression with constant and maximum distance method. The results indicate that the most efficient estimators were obtained with models that consider all sizes and forms of the uniformity trial (n=63). Segmented regression and linear regression with constant models produced the best estimators of the optimal size of the experimental plot: 72.16 and 93.22 m², respectively. With the other three methods, considerable and inconsistent differences in the sizes of the experimental plot were obtained. With the methods of maximum curvature and maximum curvature of the coefficient of variation, the results were so small: 14.01 and 12.5 m², respectively, that they are inadequate to carry out research in sugarcane; on the contrary, with the method of maximum distance, the size obtained was 157.48 m², statistically and economically inefficient. Therefore, the linear regression with constant and quadratic regression with constant models are appropriate for determining the experimental plot size in sugarcane. It was concluded that the recommended size to be used in the area is 72 m². This research was completed in December 2021. [ABSTRACT FROM AUTHOR]

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]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Risk measurements are clearly central to risk management, in particular for banks, (re)insurance companies, and investment funds. The question of the appropriateness of risk measures for evaluating the risk of financial institutions has been heavily debated, especially after the financial crisis of 2008/2009. Another concern for financial institutions is the pro-cyclicality of risk measurements. In this paper, we extend existing work on the pro-cyclicality of the Value-at-Risk to its main competitors, Expected Shortfall, and Expectile: We compare the pro-cyclicality of historical quantile-based risk estimation, taking into account the market state. To characterise the latter, we propose various estimators of the realised volatility. Considering the family of augmented GARCH(p , q) processes (containing well-known GARCH models and iid models, as special cases), we prove that the strength of pro-cyclicality depends on the three factors: the choice of risk measure and its estimators, the realised volatility estimator and the model considered, but, no matter the choices, the pro-cyclicality is always present. We complement this theoretical analysis by performing simulation studies in the iid case and developing a case study on real data. [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]

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.

In this work, a nonlinear estimator-based robust controller is designed for the position and yaw control of a quadrotor with uncertainty estimation. This controller ensures the tracking of desired references in the presence of parameters variation and external disturbances, making use of high-order sliding mode (HOSM) estimators to estimate these perturbations that can be canceled by the control, thus improving the dynamic behavior of the controlled system. Its performance is evaluated making use of a Simcenter Amesim quadrotor based on physical models generated from experimental data in a co-simulation framework with Matlab–Simulink used to implement the designed controller with FPGA implementation. A challenging and generic maneuver with time-varying wind disturbances and uncertainty model parameters is considered. [ABSTRACT FROM AUTHOR]

In the presence of multicollinearity and outliers, the ordinary least squares estimator remains inconsistent and unreliable. Several estimators have been proposed that can co-handle the problems of multicollinearity and outliers simultaneously. However, there is still a need to explore some other robust methods when the two anomalies appear in the linear regression model and recommend it to end users of statistics. Therefore, this study proposed Robust-M New Two Parameter (RNTP) and examined its performance over some already existing ones in the presence of multicollinearity and outliers in the x-direction. The theoretical expression under some conditions was established to showcase the new estimator's superiority. A simulation study was carried out alongside some factors to show that the RNTP is better than all other estimators considered in the study. The simulation study results revealed that RNTP outperformed other estimators in the study using the minimum MSE as the criterion. Likewise, real-life data was applied to affirm this claim.

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 cθ,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=nin,i=1,⋯, where ni 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.

Copyright of Journal of Administration & Economics is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

In this work, a nonlinear estimator-based robust controller is designed for the position and yaw control of a quadrotor with uncertainty estimation. This controller ensures the tracking of desired references in the presence of parameters variation and external disturbances, making use of high-order sliding mode (HOSM) estimators to estimate these perturbations that can be canceled by the control, thus improving the dynamic behavior of the controlled system. Its performance is evaluated making use of a Simcenter Amesim quadrotor based on physical models generated from experimental data in a co-simulation framework with Matlab–Simulink used to implement the designed controller with FPGA implementation. A challenging and generic maneuver with time-varying wind disturbances and uncertainty model parameters is considered.

The present article aims to shows that obtaining the best estimator does not depend on estimators' terms and functions. To prove this situation, it is shown that there is no difference between the existing estimators and the proposed estimator by proposing new sophisticated estimators. Besides, theoretical, graphical, empirical and simulation studies are conducted to confirm the efficiency of these proposed estimators with the help of the method used by Wolter (2007). Also, the MSE values are evaluated by using the power coefficient of the interested estimators. In data sets with the known correlation coefficient, it is possible to determine which power coefficient estimator is better without computing the MSE value. [ABSTRACT FROM AUTHOR]

Multivariate exponentially weighted moving average (MEWMA) control chart with five different estimators as population covariance matrix is rarely applied to monitor small fluctuations in the statistical process control. In this article, mathematical models of the five estimators (S1, S2, S3, S4, S5) are established, with which the relevant MEWMA control charts are obtained, respectively. Thereafter, the process monitoring performance of the five control charts is simulated. And the simulation results show that the S4 estimator-based MEWMA control chart is of the best performance both in step offset failure mode and ramp offset failure mode. Since the inline process monitoring of photovoltaic manufacturing is intended to be a problem of multivariate statistics process analysis, the feasibility and effectiveness of the proposed model are elaborated in the case study during the cell testing and sorting process control for the fabrication of multicrystalline silicon solar cells. [ABSTRACT FROM AUTHOR]

Old metallic bridges are prone to high levels of structural degradation due to their long service life, therefore, it is essential to study strengthening techniques. Implementation of injection bolts to replace riveted joints exposed to fatigue damages has been studied. The mechanical performance of injection bolts has been studied mainly based on quasi‐static or creep tests. The objective of this paper is comparison of fatigue resistance between bolted joints with preloaded injection bolts opposing bolted joints with preloaded high strength bolts. Specimens were defined aiming to reproduce as close as possible a strengthening scenario. Single and double lap joints were tested. The fatigue phenomenon has a stochastic nature and therefore robust models are necessary to estimate parameters of distribution functions. Therefore, different estimation methodologies were implemented to determine the parameters of Weibull distribution. The main goal was to define reliable S‐N design curves. Results showed that the use of preloaded injection bolts contribute to increase the fatigue life and to reduce the scatter, mainly for double lap joints. It is shown that S‐N curves of EN1993‐1‐9 do not represent the fatigue strength of structural details with old metallic materials. [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]

Abstract In this paper, estimators of the Nakagami-lognormal (NL) distribution based on the method of log-moments have been derived and thoroughly analyzed. Unlike maximum likelihood (ML) estimators, the log-moment estimators of the NL distribution are obtained using straightforward equations with a unique solution. Also, their performance has been evaluated using the sample mean, confidence regions and normalized mean square error (NMSE). The NL distribution has been extensively used to model composite small-scale fading and shadowing in wireless communication channels. This distribution is of interest in scenarios where the small-scale fading and the shadowing processes cannot be easily separated such as the vehicular environment.

The evaluation of covariance matrix estimators is very important for portfolio analysis and risk management. The Monte Carlo synthetic sample based performance evaluation method proposed by this article can avoid the main shortcomings of statistical and economic methods which are widely used in the existing literature. The proposed method does not need the true covariance and does not need to introduce the performance of the out-of-sample portfolios. It is an intuitive, effective and robust measure for both simulation and empirical analysis. [ABSTRACT FROM AUTHOR]

Researchers face many choices when conducting large-scale multisite individually randomized control trials. One of the most common quantities of interest in multisite RCTs is the overall average effect. Even this quantity is non-trivial to define and estimate. The researcher can target the average effect across individuals or sites. Furthermore, the researcher can target the effect for the experimental sample or a larger population. If treatment effects vary across sites, these estimands can differ. Once an estimand is selected, an estimator must be chosen. Standard estimators, such as fixed-effects regression, can be biased. We describe 15 estimators, consider which estimands they are appropriate for, and discuss their properties in the face of cross-site effect heterogeneity. Using data from 12 large multisite RCTs, we estimate the effect (and standard error) using each estimator and compare the results. We assess the extent that these decisions matter in practice and provide guidance for applied researchers. [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]

In the present study, generalized classes of estimators for estimating population mean, ratio and product of two population means using rank of auxiliary character in presence of non-response are proposed. The bias and mean square error of proposed classes of estimators are obtained and their performances examined. Specific conditions under which the members of proposed classes of estimators attain minimum mean square error are obtained. Comparative study of the proposed classes of estimators with the relevant estimators is carried out. An empirical study is given to justify the efficiency of the proposed classes of estimators. [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]

The quantized neural networks (QNNs) can be useful for neural network acceleration and compression, but during the training process they pose a challenge: how to propagate the gradient of loss function through the graph flow with a derivative of 0 almost everywhere. In response to this non-differentiable situation, we propose a novel Asymptotic-Quantized Estimator (AQE) to estimate the gradient. In particular, during back-propagation, the graph that relates inputs to output remains smoothness and differentiability. At the end of training, the weights and activations have been quantized to low-precision because of the asymptotic behaviour of AQE. Meanwhile, we propose a M-bit Inputs and N-bit Weights Network (MINW-Net) trained by AQE, a quantized neural network with 1–3 bits weights and activations. In the inference phase, we can use XNOR or SHIFT operations instead of convolution operations to accelerate the MINW-Net. Our experiments on CIFAR datasets demonstrate that our AQE is well defined, and the QNNs with AQE perform better than that with Straight-Through Estimator (STE). For example, in the case of the same ConvNet that has 1-bit weights and activations, our MINW-Net with AQE can achieve a prediction accuracy 1.5% higher than the Binarized Neural Network (BNN) with STE. The MINW-Net, which is trained from scratch by AQE, can achieve comparable classification accuracy as 32-bit counterparts on CIFAR test sets. Extensive experimental results on ImageNet dataset show great superiority of the proposed AQE and our MINW-Net achieves comparable results with other state-of-the-art QNNs. [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]

Perpetually increasing human population has kept natural resources and biodiversity under a continuum of anthropogenic pressures compelling wildlife managers to keep a count of what and how many are there to be conserved and protected. We present here baseline information about the abundance of some wild faunal species counted on predetermined belt transects of varied lengths on three consecutive days during summer 2012 in the Jasrota Wildlife Sanctuary, Jammu & Kashmir. Rhesus macaque (Macaca mulatta) was the most abundant species with the highest mean ecological density (individuals/km2) of 146.9 mean ± 1.47 90% CI followed by the red jungle fowl (Gallus gallus) (46.46 ± 0.84 90% CI). Amongst the ungulate species observed, the muntjac (Muntiacus muntjak) had the highest mean density (9.49 ± 1.52 90% CI). Bounded Count method used to estimate population size produced the highest estimate for macaques as 135 individuals (90% CI: lower bound = 117 and upper bound = 279) whereas the smallest population estimate obtained was 5 individuals (90% CI: lower bound = 3 and upper bound = 21) for the wild pig. The highest grouping tendency was found for the rhesus macaque (10.88 mean ± 5.74 SD) followed by the red jungle fowl (2.65 ± 2.07) and the Indian peafowl (2.26 ± 1.72). The Indian muntjac was observed either solitary or in very small groups (1.26 ± 0.45). The abundance estimates obtained in the study area can be considered conservative and may be helpful in developing future management strategies. We discuss about the limitations and precision of the abundance estimates obtained.

The Langmuir isotherm is a nonlinear regression model, being one of the most applied in adsorption studies. In this type of study, the data are collected over time, which can provide correlated errors; in addition, the collection is not always done in an equidistant way, which may influence the estimation of model parameters. One way of modelling the dependent errors in a regression model is to use an autoregressive process that assumes that the observations are performed at equidistant intervals. However, the definition of the independent variable is often performed at irregular intervals, causing a reduction of information obtained from the dataset. One possible improvement in the adjustment quality of these models is the use of the irregular autoregressive process. The objective of this work was to compare the estimates of isotherm parameters with different irregular and regular autoregressive error structures, considering the positive autocorrelation in different sample sizes, error autocorrelation values and positioning of non-equidistant observations. It was found that there is a need to respect the assumptions of the model. The irregular autoregressive model is more appropriate because it is mostly more precise and accurate, especially when non-equidistance occurs in the initial third.

Clustering is basically one of the major sources of primary data mining tools. It makes researchers understand the natural grouping of attributes in datasets. Clustering is an unsupervised classification method with the major aim of partitioning, where objects in the same cluster are similar, and objects which belong to different clusters vary significantly, with respect to their attributes. However, the classical Standardized Euclidean distance, which uses standard deviation to down weight maximum points of the ith features on the distance clusters, has been criticized by many scholars that the method produces outliers, lack robustness, and has 0% breakdown points. It also has low efficiency in normal distribution. Therefore, to remedy the problem, we suggest two statistical estimators which have 50% breakdown points namely the Sn and Qn estimators, with 58% and 82% efficiency, respectively. The proposed methods evidently outperformed the existing methods in down weighting the maximum points of the ith features in distance-based clustering analysis. [ABSTRACT FROM AUTHOR]

We consider a stationary linear AR(p) model with observations subject to gross errors (outliers). The distribution of outliers is unknown and arbitrary, their intensity is γn−1/2 with an unknown γ, n is the sample size. The autoregression parameters are unknown, they are estimated by any estimator which is n1/2-consistent uniformly in γ ≤ Γ < ∞. Using the residuals from the estimated autoregression, we construct a kind of empirical distribution function (e.d.f.), which is a counterpart of the (inaccessible) e.d.f. of the autoregression innovations. We obtain a stochastic expansion of this e.d.f., which enables us to construct a test of Pearson's chi-square type for testing hypotheses about the distribution of innovations. We establish qualitative robustness of this test in terms of uniform equicontinuity of the limiting level with respect to γ in a neighborhood of γ = 0. [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]

Many existing signal-to-noise ratio (SNR) estimators were designed and evaluated for conventional one-hop communications systems. However, for a relaying system, it is the end-to-end SNR that determines the system performance. In this paper, we will fill this gap by evaluating the performances of the existing SNR estimators in a dual-hop relaying system used for each hop. The probability density functions of the SNR estimators are first derived, whose parameters are fitted as functions of the sample size and the true value of SNR. Using them, the cumulative distribution functions of the end-to-end SNR and the bit error rate performance for a relaying system are derived. Numerical results show that the squared signal-to-noise variance estimator has the best performance for small SNRs and the second-order fourth-order moments estimator has the best performance for large SNRs, while the signal-to-variation ratio estimator has the worst performance, among the existing SNR estimators, for AF relaying systems. [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]

Assume that a real linear regression model is presented in certain mis-specified form. Under this situation, the predictions and estimations of all unknown parameters in the mis-specified model will lead to wrong conclusions in the statistical inference of the real model. The purpose of this paper is to characterize the relationships between the best linear unbiased predictors (BLUPs) of all unknown parameters under a real linear model and its mis-specified forms via some exact algebraic tools in matrix theory. [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]

ABSTRACT Estimating species richness with herbarium data and new collections allows us to understand the distribution of diversity. We investigated the accuracy of lycophyte and fern sampling along a vegetation gradient in the subtropical Atlantic Forest in southern Brazil. We compiled lycophyte and fern collection metadata and estimated species richness and assessed sampling accuracy for sixty 50 x 50 km units using ACE, Chao 1, Chao 2, Jackknife 1 and Jackknife 2 estimators. We compiled data for 12,779 fern specimens of 441 species, 67 of which were sampled in only one unit (singletons) and 35 in two units (duplicates). Of the 60 units examined, only 11 had observed values that were above 70% of their estimated values, and 14 had observed levels between 65-70% of the estimated values, meaning that 35 units had a sampling accuracy of less than 65%. In spite of the long history of lycophyte and fern collecting in the study area, there remain units with a lower than expected sampling accuracy for a subtropical forest. This finding indicates that a sizeable collection effort is needed in order to discover the actual distribution of species before the effects of fragmentation and deforestation become permanent.