Unbiased Property Research Articles

Mutually Unbiased Property of Special Entangled Bases

Mutually Unbiased Property of Special Entangled Bases

Couples' social characteristics, family planning, and unwanted pregnancy risk: Evidence from two Nigerian Demographic and Health Surveys.

Increased cases of child abandonment, homeless and street children in developing countries are traceable to proportionate rates of unwanted pregnancies. Such pregnancies impose hardships on households and increased social vices in society. In Nigeria, baby factories are continually being discovered in almost every state, thereby raising concerns about the exposure of women to the dangers of unwanted pregnancies. In order to contribute to the government's efforts to control unwanted pregnancies in Nigeria, this study examined the effects of couples' social characteristics and family planning methods on unwanted pregnancies in Nigeria. We applied the Ordinary Least Squares (OLS) method of regression analysis because of its efficiency and unbiased property in statistical analysis. Survey data from the 2013 and 2018 Nigeria's Demographic and Health Surveys were used in the analysis. Results indicated that the more family planning methods are used in Nigeria, the fewer unwanted pregnancies will occur. Furthermore, the social characteristics of men are more important than those of women in predicting unwanted pregnancies. Based on other findings, literacy campaign in the Northern Nigeria where literacy rate is low, more counselling on the importance of modern family planning, and use of different strategies in population health awareness campaign across the different regions in Nigeria is encouraged.

Varying coefficient frailty models with applications in single molecular experiments.

Motivated by an analysis of single molecular experiments in the study of T-cell signaling, a new model called varying coefficient frailty model with local linear estimation is proposed. Frailty models have been extensively studied, but extensions to nonconstant coefficients are limited to spline-based methods that tend to produce estimation bias near the boundary. To address this problem, we introduce a local polynomial kernel smoothing technique with a modified expectation-maximization algorithm to estimate the unknown parameters. Theoretical properties of the estimators, including their unbiased property near the boundary, are derived along with discussions on the asymptotic bias-variance trade-off. The finite sample performance is examined by simulation studies, and comparisons with existing spline-based approaches are conducted to show the potential advantages of the proposed approach. The proposed method is implemented for the analysis of T-cell signaling. The fitted varying coefficient model provides a rigorous quantification of an early and rapid impact on T-cell signaling from the accumulation of bond lifetime, which can shed new light on the fundamental understanding of how T cells initiate immuneresponses.

Predicting primary energy consumption using NDGM(1,1,k,c) model with Simpson formula

Energy consumption plays a key role in economics development for all countries. Catching the future trend of energy consumption is very important for the governments and energy companies. In this paper, the primary energy consumption of Saudi Arabia, India, Philippines and Vietnam are systematically studied by various forecasting models. Based on the actual data from 2006 to 2016, a novel grey forecasting model termed NDGM_S (1,1,k,c) is proposed where the Simpson numerical integration formula is applied to construct the background value. The time response function and the restored value of the present model are deduced, and then the unbiased property is proved. As shown in the computational results, the NDGM_S (1,1,k,c) model can achieve better prediction accuracy than other forecasting models, and it is quite suitable for predicting sequence with homogeneous/non-homogeneous exponential law.

Appraisal Accuracy and Automated Valuation Models in Rural Areas

Accurate and unbiased property value estimates are essential to credit risk management. Along with loan amount, they determine a mortgage’s loan-to-value ratio, which captures the degree of homeowner equity and is a key determinant of borrower credit risk. For home purchases, lenders generally require an independent appraisal, which, in addition to a home’s sales price, is used to calculate a value for the underlying collateral. A number of empirical studies have shown that property appraisals tend to be biased upwards, and over 90 percent of the time, either confirm or exceed the associated contract price. Our data suggest that appraisal bias is particularly pervasive in rural areas where over 25 percent of rural properties are appraised at more than five percent above contract price. Given this significant upward bias, we examine a host of alternate valuation techniques to more accurately estimate rural property values.

Volatility behavior of asset returns based on robust volatility ratio: Empirical analysis on global stock indices

AbstractIn this paper, we come up with an alternate theoretical proof for the independence and unbiased property of extreme value robust volatility estimator with respect to the standard robust vol...

Conventional Bell Basis in PT-symmetric Quantum Theory

Theories defined by non-Hermitian PT-symmetric Hamiltonians exhibit strange and unexpected properties at the classical as well as at the quantum level. In this paper the conventional Bell basis is discussed using the CPT-inner product for a general two-state system in PT-symmetric quantum theory. The conventional Bell basis has many applications in quantum information theory because of the mutual orthogonality, normlization, and maximally entangled degree of each vector in the basis. This paper analyzes four properties of the Bell basis in PT-symmetric quantum theory: CPT-orthogonality, CPT-norm, CPT-entanglement degree and CPT-mutual unbiased property with other maximally entangled bases. The conventional Hermitian Bell states are not CPT-normalized and are not maximally entangled states in PT-symmetric quantum theory. The orthogonality still holds for four of the six pairs of Bell states under the CPT-inner product. Furthermore, the mutually unbiased property with another maximally entangled basis is preserved at the level of twenty five percent.

Mutually Unbiased Property of Maximally Entangled Bases and Product Bases in ℂ d ⊗ ℂ d $\mathbb {C}^{d}\otimes \mathbb {C}^{d}$

We investigate mutually unbiased property between maximally entangled bases and product bases in bipartite systems $\mathbb {C}^{d} \otimes \mathbb {C}^{d}$ . We first visualize the description of $p_{1}^{a_{1}}-1$ -member mutually unbiased maximally entangled bases(MUMEBs) in $\mathbb {C}^{d} \otimes \mathbb {C}^{d}\ $ , while $d=p_{1}^{a_{1}}p_{2}^{a_{2}}...p_{s}^{a_{s}}$ , $3\leq p_{1}^{a_{1}}\leq p_{2}^{a_{2}}\leq ...\leq p_{s}^{a_{s}}$ , $p_{1}^{a_{1}},...,p_{s}^{a_{s}}$ are distinct primes, which was proposed by Liu et al. (Quantum Inf. Process. 16(6), 159, 2017). We then establish two more mutually unbiased product bases which are also mutually unbiased to the above $p_{1}^{a_{1}}-1$ MUMEBs, thus we present $p_{1}^{a_{1}}+ 1$ mutually unbiased bases(MUBs) in $\mathbb {C}^{d} \otimes \mathbb {C}^{d}\ $ . We also show the concrete construction of those MUBs in bipartite systems $\mathbb {C}^{3} \otimes \mathbb {C}^{3}\ $ , $\mathbb {C}^{4} \otimes \mathbb {C}^{4}\ $ , $\mathbb {C}^{5} \otimes \mathbb {C}^{5}\ $ and $\mathbb {C}^{12} \otimes \mathbb {C}^{12}$ .

Two Types of Maximally Entangled Bases and Their Mutually Unbiased Property in ℂ d ⊗ ℂ d ′ $\mathbb {C}^{d}\otimes \mathbb {C}^{d^{\prime }}$

We first construct a new maximally entangled basis in bipartite systems $\mathbb {C}^{d} \otimes \mathbb {C}^{kd}\ (k\in Z^{+})$ which is diffrent from the one in Tao et al. (Quantum Inf. Process. 14, 2291 (2015)), then we generalize such maximally entangled basis into arbitrary bipartite systems $\mathbb {C}^{d} \otimes \mathbb {C}^{d^{\prime }}$ . We also study the mutual unbiased property of the two types of maximally entangled bases in bipartite systems $\mathbb {C}^{d} \otimes \mathbb {C}^{kd}$ . In particular, explicit examples in $\mathbb {C}^{2} \otimes \mathbb {C}^{4}$ , $\mathbb {C}^{2} \otimes \mathbb {C}^{8}$ and $\mathbb {C}^{3} \otimes \mathbb {C}^{3}$ are presented.

Quasi-Stochastic Integration Filter for Nonlinear Estimation

In practical applications, numerical instability problem, systematic error problem caused by nonlinear approximation, and nonlocal sampling problem for high-dimensional applications, exist in unscented Kalman filter (UKF). To solve these problems, a quasi-stochastic integration filter (QSIF) for nonlinear estimation is proposed in this paper. nonlocal sampling problem is solved based on the unbiased property of stochastic spherical integration rule, which can also reduce systematic error and improve filtering accuracy. In addition, numerical instability problem is solved by using fixed radial integration rule. Simulations of bearing-only tracking model and nonlinear filtering problem with different state dimensions show that the proposed QSIF has higher filtering accuracy and good numerical stability as compared with existing methods, and it can also solve nonlocal sampling problem effectively.

Unbiased Direct DGM(1,1) Model of Non-Homogeneous Exponential Sequence and its Application in Electric Load Forecasting

The traditional GM (1,1) has been used widely in the load forecasting, however, there are many defects in the GM (1,1). In order to overcome these defects and expand the application scope of the grey model in load forecasting, a new load forecasting method based on DDGM(1,1) is presented. First, the recursive solution of DDGM(1,1) is given. Then, based on the solution, the unbiased property for non-homogeneous exponential incremental sequence of this model is proved. It is applied to some load forecasting and is compared with the traditional GM (1,1) model.The results show that the presented forecasting method is superior obviously to traditional methods, and it can be used for the approximate non-homogeneous exponential incremental load forecasting generally.

Without Bias? Government Policy That Creates Fair and Equitable Property Tax Assessments

Ensuring accurate, unbiased residential property tax assessments is an important, and contentious, issue facing communities today, particularly in this era of fiscal and economic crisis. Furthermore, this question is critical because it involves important social justice issues. Past studies have shown inequities by race, class, and location with some neighborhoods paying more of their fair share in property taxes. This study looks at the reliability of tax-assessment data in a medium-sized American city—Louisville, Kentucky. In an effort to compare various housing data sources, this study contrasts three measures of neighborhood median housing value for 170 census tracts in Louisville in the year 2000: self-reported values (U.S. Census); sales prices (MLS); and tax assessments from the county’s property valuation administrator. We find that the effects of various neighborhood characteristics on the three property measures do not vary by measure. Furthermore, an assessments-to-sale ratio does not vary by traditional indicators of neighborhood distress including racial composition and proximity to the inner city. We conclude that assessments in Louisville are not biased by neighborhood and argue that several aspects of policy and administration present in Louisville make for more accountable and fair assessment practices—including tough state statutes, the election of the chief assessor, the use of aerial technology to view improvements, public transparency, the use of sales prices in reassessing properties, and the willingness to reassess downward communities hit hardest by the foreclosure crisis.

A generalized false discovery rate in microarray studies

The problem of identifying differentially expressed genes is considered in a microarray experiment. This motivates us to involve an appropriate multiple testing setup to high dimensional and low sample size testing problems in highly nonstandard setups. Family-wise error rate (FWER) is too conservative to control the type I error, whereas a less conservative false discovery rate has received considerable attention in a wide variety of research areas such as genomics and large biological systems. Recently, a less conservative method than FDR, the k -FDR, which generalizes the FDR has been proposed by Sarkar (2007). Most of the current FDR procedures assume restrictive dependence structures, resulting in being less reliable. The purpose of this paper is to address these very large multiplicity problems by adopting a proposed k -FDR controlling procedure under suitable dependence structures and based on a Poisson distributional approximation in a unified framework. We compare the performance of the proposed k -FDR procedure with that of other FDR controlling procedures, with an illustration of the leukemia microarray study of Golub et al. (1999) and simulated data. For power consideration, different FDR procedures are assessed using false negative rate (FNR). An unbiased property is appraised by FDR ≤ α and a higher value of 1 − ( FDR + FNR ) . The proposed k -FDR procedure is characterized by greater power without much elevation of k -FDR.

THE COMPUTING METHOD AND UNBIASED PROPERTY OF TWO-STAGE ESTIMATE IN THE GROWTH CURVE MODEL WITH RANDOM EFFECTS

Firstly,a simple computing method is given for the two-stage estimate of regression coefficient matrix in the Growth Curve Model with Random Effects. Then the unbiased property of the two-stage estimate was become an unbiased estimate is proved.Finally, an example of dental growth data is given.

$L_2$--$E$ FIR Filters for Deterministic Continuous-Time-State Space Signal Models

In this paper, a new L2 - E performance criterion is introduced, which is represented as a gain between the disturbance during the recent time interval and the current estimation error. Based on the L2 - E performance criterion, the L2 - E finite impulse response (FIR) filter (LEFF) is proposed for deterministic continuous-time-state space signal models without requiring the stochastic information such as variances and means. The LEFF is designed to minimize the maximum value of the L2 - E performance criterion together with prior constraints such as linearity, unbiased property in the deterministic sense, and FIR structure, simultaneously. Via simulation, the LEFF is compared with other deterministic infinite impulse response (IIR) filters such as the Hinfin and L2 - Linfin filters.

$L_{2}-E$ FIR Smoothers for Deterministic Discrete-Time State–Space Signal Models

In this note, a new type of L2 - E performance criterion for a fixed-lag smoother is introduced, which is given by a gain between the energy of the external disturbances during the recent time horizon and the estimation error at the fixed-delayed time from the current one. By minimizing the maximum value of the L2 - E performance criterion, the L2-E finite impulse response (FIR) smoother(LEFS) is obtained for a deterministic discrete-time state space signal model. The LETS is designed to meet requirements such as linearity and FIR structure with respect to measured inputs and outputs, unbiased property in the deterministic sense, and independence of any state information simultaneously. The proposed LETS is represented in a batch form for simplicity. The LETS is compared with a conventional H infin Infinite Impulse Response (IIR) smoother via simulation

Minimax L2-E2 FIR filters for deterministic continuous-time state space signal models

In this paper, a new minimax L2-E2 performance criterion is introduced, which is represented as a worst case gain between the disturbance during the recent time interval and the current estimation error. Mnimax L2-E2 FIR filter (MLEFF) is proposed for deterministic continuous-time state space signal models. The MLEFF is designed to minimize the maximum value of the L2-E2 performance criterion together with linearity, unbiased property in the deterministic sense, FIR structure, and independence of the initial state information, simultaneously. The MLEFF is shown to be robust with respect to model uncertainties due to FIR structure and disturbances due to the worst case design. For efficient implementation, it is shown that the MILEFF can be expressed recursively. It is also shown to be optimal for the performance criterion for each component of the state.

Spatial rainfall estimation by linear and non-linear co-kriging of radar-rainfall and raingage data

The feasibility of linear and nonlinear geostatistical estimation techniques for optimal merging of rainfall data from raingage and radar observations is investigated in this study by use of controlled numerical experiments. Synthetic radar and raingage data are generated with their hypothetical error structures that explicitly account for sampling characteristics of the two sensors. Numerically simulated rainfall fields considered to be ground-truth fields on 4×4 km grids are used in the generation of radar and raingage observations. Ground-truth rainfall fields consist of generated rainfall fields with various climatic characteristics that preserve the space-time covariance function of rainfall events in extratropical cyclonic storms. Optimal mean areal precipitation estimates are obtained based on the minimum variance, unbiased property of kriging techniques under the second order homogeneity assumption of rainfall fields. The evaluation of estimated rainfall fields is done based on the refinement of spatial predictability over what would be provided from each sensor individually. Attention is mainly given to removal of measurement error and bias that are synthetically introduced to radar measurements. The influence of raingage network density on estimated rainfall fields is also examined.

On Small-Sample Estimation

This paper discusses some of the concepts underlying small sample estimation and reexamines, in particular, the current notions on estimation. Alternatives to the usual unbiased property are examined with respect to invariance under simultaneous one-to-one transformation of parameter and estimate; one of these alternatives, closely related to the maximum likelihood method, seems to be new. The property of being unbiased in the likelihood sense is essentially equivalent to the statement that the estimate is a maximum likelihood estimate based on some distribution derived by integration from the original sampling distribution, by virtue of a hereditary property of maximum likelihood estimation. An exposition of maximum likelihood estimation is given in terms of optimum pairwise selection with equal weights, providing a type of rationale for small sample estimation by maximum likelihood.