Variance Estimation Method Research Articles

An Iterative Kalman Filter with Reduced-Biased Kalman Gain for Single Channel Speech Enhancement in Non-stationary Noise Condition

This paper presents an iterative Kalman filter (IT-KF) with a reduced-biased Kalman gain for single channel speech enhancement in Non-stationary Noise Conditions (NNCs). The proposed IT-KF aims to offset the bias in Kalman gain through efficient parameter estimation leading to improve the speech enhancement performance. To do this, we introduce a Decision Directed (DD) and a posteriori SNR based noise variance estimation method controlled through Speech Activity Detector (SAD). The proposed SAD incorporates a majority voting of three distinct SAD fusions. The LPC parameters are computed from the pre-smoothing of noisy speech. With these initial estimated parameters, an IT-KF processes the noisy speech at first iteration. The parameters are re-estimated from the processed speech, re-adjust the Kalman gain, and the process is repeated at second iteration. It is shown that the adjusted Kalman gain enables the IT-KF to minimize the remaining artifacts of the processed speech, yielding the enhanced speech. Extensive simulation results reveal that the proposed method outperforms other benchmark methods in NNCs for a wide range of SNRs.

A review on liquid level measurement techniques using mathematical models and field sensors in blast furnace

Stable blast furnace operation requires proper drainage of liquid from the hearth. The estimation of drainage rate and hearth liquid level are of utmost importance for understanding the underlying hearth phenomena. The present review gives an insight of the need for measurement of the hearth liquid level and further throws light on various methods of estimation of hearth liquid level in-depth, which is primarily divided into 2 categories namely; model-based estimation and sensor-based estimation. Although the model-based estimation and sensor-based estimation have their own advantages and disadvantages, an integrated system comprising of both methods could potentially facilitate operators to reveal the state of hearth, which is unless not available with the implementation of a single method of estimation. Furthermore, the challenges in the estimation and the prospects for determining the reliable hearth liquid level measurement in blast furnace are discussed. The article is presented to give prospect of encountering the drift occurring in estimation of liquid level, which leads to inaccurate prediction and simulation with the industrial blast furnace. Lastly, the article gives recommendation for improving the liquid level estimation methods in blast furnace.

Effect of Different Variable Selection and Estimation Methods on Performance of Fault Diagnosis

Effect of Different Variable Selection and Estimation Methods on Performance of Fault Diagnosis

Partial linear models with general distortion measurement errors

This paper considers partial linear regression models when neither the response variable nor the covariates can be directly observed, but are instead measured with both multiplicative and additive distortion measurement errors. We propose conditional variance estimation methods to calibrate the unobserved variables. A profile least-squares estimator associated with the asymptotic results and confidence intervals is then proposed. To do hypothesis testing of the parameters, a restricted estimator under the null hypothesis and a test statistic are proposed. The asymptotic properties of the estimator and the test statistic are also established. Further, we employ the smoothly clipped absolute deviation penalty to select relevant variables. The resulting penalized estimators are shown to be asymptotically normal and have the oracle property. Estimation, hypothesis testing, and variable selection are discussed under the scenario of multiplicative distortion alone. Simulation studies demonstrate the performance of the proposed procedure and a real example is analyzed to illustrate its applicability.

Import effect on technical efficiency of cargo ports: evidence from Italy, Slovenia, and Croatia

The objective of this research is to investigate to what extent the 26 ports included in this study are technically efficient and how different factors can influence efficiency. Through the model presented, we examine the hypothesis which states that imports affect ports' competitiveness and efficiency, so they should be included in the deterministic part of the model because ports are aware of trade impact when making infrastructure changes. The research used the True-fixed Effect Stochastic Frontier model for panel data with time variance (Greene 2005) and implemented the Cobb- Douglas production function with truncated normal distribution and the maximum-likelihood dummy variable estimation method. The model outcome is presented through values of technical efficiency for ten years, and it monitors the Croatian, Italian and Slovenian ports’ response at these significant periods of the European economy. Keywords: ports, technical efficiency, Stochastic frontier model, true fixed-effects DOI : 10.7176/JESD/10-22-16 Publication date: November 30 th 2019

Quantifying the association between acute leukemia and serum zinc, copper, and selenium: a meta-analysis

Acute leukemia is a clonal malignant disorder that occurs when immature blast cells accumulate in bone marrow. Zinc (Zn) and copper (Cu) are related to normal lymphocyte maturation and immune function regulation. Selenium (Se) is protective against oxidative damage. The aim of this meta-analysis is to statistically synthesize results from studies that have investigated the levels of Zn, Cu, and Se in acute leukemia patients. The effect size, delta, was used to standardize the raw data. The robust variance estimation (RVE) method was performed to measure the pooled effect size and variance. Results suggest significant negative differences for levels of serum Zn (p < .05, delta = −1.21; 95% CI, −2.13-−0.28) and Se (p < .05, delta = −1.84; 95% CI, −3.39-−0.29) and significantly positive differences between serum Cu levels (p < .01, delta = 1.94; 95% CI, 1.02–2.87) in acute leukemia, as compared to the controls.

Diabetes Mellitus And Glycosylated Hemoglobin A1c

Diabetes mellitus is a global health problem in 21st century. The incidence of diabetes mellitus is in rise and is estimated to be more prevalent in coming years. It is becoming one of the most common non-infectious and non-neoplastic causes of morbidity and mortality. Various complications are associated with diabetes mellitus. With control of blood glucose level, complications of diabetes mellitus can be minimized. In different time frame, different parameters and level have been used to diagnose diabetes. Glycosylated hemoglobin A1c is one of the reliable indicator of chronic hyperglycemia. In 2011, World Health Organization has included HbA1c in the diagnostic criteria. Various methods are used to detect the level of HbA1c. Glycosylated hemoglobin is being used in the management of diabetes mellitus. Various studies have shown its prognostic implication in micro and macrovascular complications. Here we discuss various methods of estimation of HbA1c, various role of HbA1c in the management of Diabetes Mellitus and limitation of the test.

Impact of the Syrian refugee influx on Turkish native workers: An ethnic enclave approach

Turkey received about 2.7 million Syrian refugees between 2011 and 2015. This paper examines the causal impact of this influx on the Turkish natives' labor market outcomes using the micro level annual Household Labor Force Surveys. The migration impact is analyzed in two distinct categories considering the motives behind the migration decision. The initial migration to the border regions is defined as the primary migration and a standard difference in differences strategy is employed to estimate the labor market impacts in those regions since the initial flow to the border regions was completely exogenous. The migration from the primary regions towards the inner regions, on the other hand, is subject to endogenous selection. Hence, I defined it as the secondary migration and developed an instrumental variables estimation method to address the selection bias following the Card (2009)'s ethnic enclave approach. I found statistically significant negative employment and wage effects on the low-skilled and less-experienced individuals in the primary migration analysis. The decline in the wages of informal workers is the main contributor of the negative wage effects. Secondary migration has no impact on the employment but there are statistically significant negative wage effects on the low-skilled and less-experienced workers.

Unbiased weighted variance and skewness estimators for overlapping returns

This article develops unbiased weighted variance and skewness estimators for overlapping return distributions. These estimators extend the variance estimation methods constructed in Bod et. al. (Applied Financial Economics 12:155-158, 2002) and Lo and MacKinlay (Review of Financial Studies 1:41-66, 1988). In addition, they may be used in overlapping return variance or skewness ratio tests as in Charles and Darné (Journal of Economic Surveys 3:503-527, 2009) and Wong (Cardiff Economics Working Papers, 2016). An example using synthetic overlapping returns from a model fit to data from the SPY S&P 500 exchange traded fund is given in order to demonstrate under which circumstances the unbiased correction becomes significant in skewness estimation. Finally, we compare the effect of the HAC weighting schemes of Andrews (Econometrica 53:817-858, 1991) as a function of sample size and overlapping return window length.

A Meta-Analytic Review of Racial Discrimination: Relationships to Health and Culture

This meta-analysis systematically reviewed 242 studies (1805 effect sizes) published between 1998 and 2015 and examined the correlations between racial discrimination, health (physical health, mental health, and substance use), and cultural outcomes (acculturation, racial identity, collective self-esteem, ethnic identity, and racial socialization) among racial-ethnic minority Americans. The relationships between several moderators were examined (e.g., gender, race) using subgroup analyses and meta-regression. The meta-analysis was conducted using Fisher’s Z with a robust variance estimation method that accounted for the dependence among correlations in the same study. Findings suggest that the direct overall relationship between racial discrimination, health, and culture was strongest for mental health (r = .207), then substance use (r = .159), culture (r = .101), and physical health (r = .067); findings were confirmed by a meta-regression that controlled for several variables. Findings also indicated that convenience samples yielded higher correlations compared to national probability samples, and Asian and Native American racial groups had stronger effects from racial discrimination than did blacks. Race, gender, and measurement characteristics are discussed, as well as research, and educational and clinical implications.

Probabilistic sampling strategy as a means of improving quality of price indices

Abstract In spite of theoretical inferiority, a large majority of statistical institutes use non-probabilistic sampling techniques in price surveys. The main disadvantage of non-probabilistic sample design is that the risk of biased results is increased. Attempting to handle this risk in the domain of service producer price indices (SPPI) of professional services, the Croatian Bureau of Statistics (CBS) developed an innovative methodology and implemented it relying on the probability proportional to size (PPS) sample design. The purpose of the paper is to evaluate the impact of the probabilistic sampling strategy on the quality of price indices as shown in the case of SPPI for professional services at the CBS. The paper outlines respective methodological upgrading of SPPI compilation at the CBS, including also the method for variance estimation. The effect of the probabilistic sampling on SPPI is analysed by comparison with traditional purposive sampling surveys. The quality of SPPI based on the probabilistic sample approach is examined by coefficients of variation and confidence intervals.

Multivariate semi-continuous proportionally constrained two-part fixed effects models and applications.

Semi-continuous data, also known as zero-inflated continuous data, have a substantial portion of responses equal to a single value (typically 0) and a continuous, right-skewed distribution among the remaining positive values. For jointly modeling multivariate clustered semi-continuous responses, the covariate effects in the positive parts can be proportionally constrained to the covariate effects in the logistic part, yielding a multivariate two-part fixed effects model. It is shown that, both theoretically and experimentally, the proportionally constrained model is more efficient than the unconstrained model in terms of parameter estimation, and thus provides a deeper understanding of the data structure when the proportionality structure holds. A robust variance estimation method is also introduced and tested under various model mis-specified cases. The proposed model is applied to data from a randomized controlled trial evaluating potential preventive effects of meditation or exercise on duration and severity of acute respiratory infection illness. The new analysis infers that meditation not only has highly significant effects on reduction of acute respiratory infection severity and duration, but also has significant effects on preventing acute respiratory infection, which was not previously reported in the literature.

Quantile Regression Analysis of Survey Data Under Informative Sampling.

For complex survey data, the parameters in a quantile regression can be estimated by minimizing an objective function with units weighted by the original design weights. However, when the complex survey sampling design is informative (i.e., when the design weights are correlated with the study variable even after conditioning on other covariates), the efficiency of this design-weighted estimator may be improved. In this article, we propose several weight-smoothing estimators for quantile regression analysis of complex survey data collected with an informative sampling design. Our new estimators incorporate nonparametric methods for modeling the weight functions and pseudo-population bootstrap methods for variance estimation. A simulation study compares, our proposed methods with the original design-based method in terms of bias, standard error, mean squared error, and confidence coverage. Our proposed estimators have smaller bias and mean squared error than does the design-based estimator. We further illustrate and compare estimators for the 1988 US National Maternal and Infant Health Survey.

Control of Corruption and Luxury Goods Consumption

SummaryThis paper investigates the effect of control of corruption on the consumption of luxury goods, after controlling other relevant determinants of luxury spending. The model is empirically tested for 32 developed and emerging economies between 2004 and 2014. Using panel fixed effects, difference generalized method of moments (GMM) and instrumental variable estimation methods, and two measures of the control of corruption (Transparency International Corruption Perceptions Index and the World Bank's Control of Corruption Index), the results show that higher levels of control of corruption decrease luxury spending. This relationship is stronger in countries with higher levels of press freedom and information transparency. These findings offer some important implications. Governments and policymakers may develop and implement regulations that increase transparency in luxury gifting and limit corruption practices. Luxury brand companies should further enhance their due diligence obligations to minimise reputational risks in the long term.

On a Modified Yule Distribution

A modified version of Yule distribution is introduced here and discuss some of its properties by deriving expressions for its probability generating function, raw moments, factorial moments etc. Certain recursion formulae for its probabilities, raw moments and factorial moments are also developed. Various methods of estimation are employed for estimating the parameters of the distribution and certain test procedures are suggested for testing the significance of the additional parameters of the distribution. The distribution has been fitted to certain real-life data sets for illustrating its usefulness, compared with certain existing models available in the literature. Further, a simulation study is conducted for assessing the performance of the maximum likelihood estimators.

Analysis of Categorical Data for Complex Surveys

SummaryThis paper reviews methods for handling complex sampling schemes when analysing categorical survey data. It is generally assumed that the complex sampling scheme does not affect the specification of the parameters of interest, only the methodology for making inference about these parameters. The organisation of the paper is loosely chronological. Contingency table data are emphasised first before moving on to the analysis of unit‐level data. Weighted least squares methods, introduced in the mid 1970s along with methods for two‐way tables, receive early attention. They are followed by more general methods based on maximum likelihood, particularly pseudo maximum likelihood estimation. Point estimation methods typically involve the use of survey weights in some way. Variance estimation methods are described in broad terms. There is a particular emphasis on methods of testing. The main modelling methods considered are log‐linear models, logit models, generalised linear models and latent variable models. There is no coverage of multilevel models.

An Investigation Into the Impact of Band Error Variance Estimation on Intrinsic Dimension Estimation in Hyperspectral Images

There have been a significant number of recent papers about hyperspectral imaging, which propose various methods for estimating the number of materials/endmembers in hyperspectral images. This is sometimes called the “intrinsic” dimension (ID) of the image. Estimation of the error variance in each spectral band is a critical first step in ID estimation. The estimated error variances can then be used to preprocess (e.g., whiten) the data, prior to ID estimation. A range of variance estimation methods have been advocated in the literature. We investigate the impact of five variance estimation methods (three using spatial information and two using spectral information) on five ID estimation methods, with the aid of four different, but semirealistic, sets of simulated hyperspectral images. Our findings are as follows: first, for all four sets, the two spectral variance estimation methods significantly outperform the three spatial methods; second, when used with the spectral variance estimation methods, two of the ID estimation methods (called random matrix theory and NWHFC) consistently outperform the other three ID estimation methods; third, the better spectral variance estimation method sometimes gives negative variance estimates; fourth, we introduce a simple correction that guarantees positivity; and fifth, we give a fast algorithm for its computation.

Comparative study on the complex samples design features using SPSS Complex Samples, SAS Complex Samples and WesVarPc

Unlike simple random sampling, complex sample designs involve additional considerations such as multistage sampling, stratification, and unequal probability of selection. A basic problem with complex surveys is in variance estimation which requires the use of approximate methods. Generally, such methods are based on either the Taylor series linearization or the replication techniques. Statistical software that use standard packages usually assume that simple random sampling of elements is inadequate for data analysis from complex surveys, especially for purpose of variance estimation. This study compares the complex sample design features produced by three statistical software packages designed to handle complex surveys (SPSS 16.0 Complex Samples, SAS 9.0 Complex Samples, and WesVarPc 5.1). Comparisons among the software are made based on the types of sample design, sampling error estimates, method of variance estimation and cost of software packages. The results of the finding show that WesVarPc can be downloaded for free from Web and offers complete basic of descriptive analyses. Although expensive, SPSS 16.0 Complex Samples and SAS 9.0 Complex Samples have been dominant in the field of data management and data analysis.

Zinc, Copper, and Iron in Oral Submucous Fibrosis: A Meta-Analysis.

Oral submucous fibrosis (OSF) is a potentially malignant disorder which causes fibrosis and inflammation of the oral mucosa. Studies have reported altered levels of trace elements in oral submucous fibrosis subjects, but findings have been inconsistent. The objective of this research is to perform a meta-analysis to summarize studies that report zinc (Zn), copper (Cu), and iron (Fe) in patients, with and without OSF. A literature search of Embase, PubMed, Cochrane Library, and Web of Science electronic databases was conducted for studies up to January 2017. A total of 34 reports met the inclusion criteria. The standardized mean difference was utilized as the effect size. The robust variance estimation method was chosen to handle dependency of multiple related outcomes in meta-analysis. There was a significant increase in the levels of Cu (effect size = 1.17, p value < 0.05, 95% confidence interval (CI): 0.164–2.171) and a significant decrease in levels of Zn (effect size = −1.95, p value < 0.05, 95% CI: −3.524 to −0.367) and Fe (effect size = −2.77, p value < 0.01, 95% CI: −4.126 to −1.406) in OSF patients. The estimation of Zn, Cu, and Fe levels may serve as additional biomarkers in the diagnosis and prognosis of OSF along with the clinical features.

Geecure: An R-package for marginal proportional hazards mixture cure models

Background and objective: Most of available software packages for mixture cure models to analyze survival data with a cured fraction assume independent survival times, and they are not suitable for correlated survival times, such as clustered survival data. The objective of this paper is to present a software package to fit a marginal mixture cure model to clustered survival data with a cured fraction.Methods: We developed an R package geecure that fits the marginal proportional hazards mixture cure (PHMC) models to clustered right-censored survival data with a cured fraction. The dependence among the cure statuses and among the survival times of uncured patients within a cluster are modeled by working correlation matrices through the generalized estimating equations, and the Expectation-Solution algorithm is used to estimate the parameters. The variances of the estimated regression parameters are estimated by either a sandwich method or a bootstrap method.Results: The package geecure can fit the marginal PHMC model where the cumulative baseline hazard function is either a two-parameter Weibull distribution or specified nonparametrically. Fitting the parametric PHMC model with the Weibull baseline hazard function on average takes less time than fitting the semiparametric PHMC model does. Two variance estimation methods are comparable in the simulation study. The sandwich method takes much less time than the bootstrap method in variance estimation.Conclusions: The package geecure provides an easy access to the marginal PHMC models for clustered survival data with a cured fraction in routine survival analysis. It is easy to use and will make the wide applications of the marginal PHMC models possible.