Multivariate Logistic-Assisted Estimators of Totals From Clustered Survey Samples

2020 ◽  
Author(s):  
Timothy L Kennel ◽  
Richard Valliant
Keyword(s):  
Asymptotic Theory ◽  
Finite Population ◽  
Linear Models ◽  
Nonlinear Models ◽  
Educational Institutions ◽  
Cluster Sampling ◽  
Difference Model ◽  
Multinomial Data ◽  
Variance Estimators ◽  
Point Estimators

Abstract Estimators based on linear models are the standard in finite population estimation. However, many items collected in surveys are better described by nonlinear models; these include variables that have binary, binomial, or multinomial distributions. We extend previous work on generalized difference, model-calibrated, and pseudo-empirical likelihood estimators to two-stage cluster sampling and derive their theoretical properties with particular emphasis on multinomial data. We present asymptotic theory for both the point estimators of totals and their variance estimators. The alternatives are tested via simulation using artificial and real populations. The two real populations are one of educational institutions and degrees awarded and one of owned and rented housing units.

scholarly journals Comparison of Variance Estimators for Systematic Environmental Sample Surveys: Considerations for Post-Stratified Estimation

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 772
Author(s):  
Bryce Frank ◽  
Vicente J. Monleon
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
National Forest Inventory ◽  
Point Estimation ◽  
Systematic Sampling ◽  
Sampling Variance ◽  
Variance Estimators ◽  
The Past ◽  
Point Estimators ◽  
Species Specific

The estimation of the sampling variance of point estimators under two-dimensional systematic sampling designs remains a challenge, and several alternative variance estimators have been proposed in the past few decades. In this work, we compared six alternative variance estimators under Horvitz-Thompson (HT) and post-stratification (PS) point estimation regimes. We subsampled a multitude of species-specific forest attributes from a large, spatially balanced national forest inventory to compare the variance estimators. A variance estimator that assumes a simple random sampling design exhibited positive relative bias under both HT and PS point estimation regimes ranging between 1.23 to 1.88 and 1.11 to 1.78 for HT and PS, respectively. Alternative estimators reduced this positive bias with relative biases ranging between 1.01 to 1.66 and 0.90 to 1.64 for HT and PS, respectively. The alternative estimators generally obtained improved efficiencies under both HT and PS, with relative efficiency values ranging between 0.68 to 1.28 and 0.68 to 1.39, respectively. We identified two estimators as promising alternatives that provide clear improvements over the simple random sampling estimator for a wide variety of attributes and under HT and PS estimation regimes.

Turbofan Engine Robust H-∞ Dynamical Output Feedback Control Design Based on LMI Method

2005 ◽  
Author(s):  
Xi Wang ◽  
Daoliang Tan ◽  
Tiejun Zheng
Keyword(s):  
Output Feedback ◽  
Linear Models ◽  
Nonlinear Models ◽  
Design Method ◽  
State Space Model ◽  
Output Feedback Control ◽  
Linear Matrix ◽  
Turbofan Engine ◽  
Engine Simulation ◽  
Linear State

This paper presents an approach to turbofan engine dynamical output feedback controller (DOFC) design in the framework of LMI (Linear Matrix Inequality)-based H∞ control. In combination with loop shaping and internal model principle, the linear state space model of a turbofan engine is converted into that of some augmented plant, which is used to establish the LMI formulations of the standard H∞ control problem with respect to this augmented plant. Furthermore, by solving optimal H∞ controller for the augmented plant, we indirectly obtain the H∞ DOFC of turbofan engine which successfully achieves the tracking of reference instructions and effective constraints on control inputs. This design method is applied to the H∞ DOFC design for the linear models of an advanced multivariate turbofan engine. The obtained H∞ DOFC is only in control of the steady state of this turbofan engine. Simulation results from the linear and nonlinear models of this turbofan engine show that the resulting controller has such properties as good tracking performance, strong disturbance rejection, and satisfying robustness.

scholarly journals On the Wave Spectrum Generated by Tropical Heating

2011 ◽  
Vol 68 (9) ◽  
pp. 2042-2060 ◽  
Author(s):  
David A. Ortland ◽  
M. Joan Alexander ◽  
Alison W. Grimsdell
Keyword(s):  
Linear Models ◽  
Nonlinear Models ◽  
Wave Spectrum ◽  
Convective Heating ◽  
Temperature Structure ◽  
Mean Flow ◽  
Quasi Biennial Oscillation ◽  
Wave Response ◽  
Zonal Wavenumber ◽  
The Mean

Abstract Convective heating profiles are computed from one month of rainfall rate and cloud-top height measurements using global Tropical Rainfall Measuring Mission and infrared cloud-top products. Estimates of the tropical wave response to this heating and the mean flow forcing by the waves are calculated using linear and nonlinear models. With a spectral resolution up to zonal wavenumber 80 and frequency up to 4 cpd, the model produces 50%–70% of the zonal wind acceleration required to drive a quasi-biennial oscillation (QBO). The sensitivity of the wave spectrum to the assumed shape of the heating profile, to the mean wind and temperature structure of the tropical troposphere, and to the type of model used is also examined. The redness of the heating spectrum implies that the heating strongly projects onto Hough modes with small equivalent depth. Nonlinear models produce wave flux significantly smaller than linear models due to what appear to be dynamical processes that limit the wave amplitude. Both nonlinearity and mean winds in the lower stratosphere are effective in reducing the Rossby wave response to heating relative to the response in a linear model for a mean state at rest.

scholarly journals Optimal Attack against Autoregressive Models by Manipulating the Environment

2020 ◽  
Vol 34 (04) ◽  
pp. 3545-3552
Author(s):  
Yiding Chen ◽  
Xiaojin Zhu
Keyword(s):  
Predictive Control ◽  
Linear Models ◽  
Nonlinear Models ◽  
Linear Quadratic Regulator ◽  
Black Box ◽  
Combine System ◽  
Linear Quadratic ◽  
Time Series Forecast ◽  
Forecast Models ◽  
Adversarial Attack

We describe an optimal adversarial attack formulation against autoregressive time series forecast using Linear Quadratic Regulator (LQR). In this threat model, the environment evolves according to a dynamical system; an autoregressive model observes the current environment state and predicts its future values; an attacker has the ability to modify the environment state in order to manipulate future autoregressive forecasts. The attacker's goal is to force autoregressive forecasts into tracking a target trajectory while minimizing its attack expenditure. In the white-box setting where the attacker knows the environment and forecast models, we present the optimal attack using LQR for linear models, and Model Predictive Control (MPC) for nonlinear models. In the black-box setting, we combine system identification and MPC. Experiments demonstrate the effectiveness of our attacks.

Sustainability engagement’s impact on tourism sector performance: linear and nonlinear models

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Amina Buallay ◽  
Jasim Al-Ajmi ◽  
Elisabetta Barone
Keyword(s):  
Significant Relationship ◽  
Linear Models ◽  
Performance Indicator ◽  
Nonlinear Models ◽  
Sustainability Reporting ◽  
Analytical Framework ◽  
Content Type ◽  
Return On Equity ◽  
Using Data ◽  
The Relationship

PurposeThis study aims to investigate the relationship between the level of sustainability reporting and tourism sector’s performance (operational, financial and market).Design/methodology/approachUsing data culled from 1,375 observations from 37 different countries for ten years (2008–2017), an independent variable derived from the environmental, social and governance (ESG score) is regressed against dependent performance indicator variables (return on assets (ROA), return on equity (ROE) and Tobin's Q (TQ)). Two types of control variables complete the regression analysis in this study: firm-specific and macroeconomic.FindingsThe findings elicited from the empirical results of the linear models demonstrate that there is a significant relationship between ESG and operational performance (ROA) and market performance (TQ). However, there is no significant relationship between ESG and financial performance (ROE). Furthermore, the results of the nonlinear models suggest that the relationship between sustainability performance and firm's profitability and valuation is nonlinear (inverted U-shape).Originality/valueThe models in this study presents a valuable analytical framework for exploring sustainability reporting as a driver of performance in the tourism sector's economies. In addition, this study highlights the tourism sector's management lacunae manifesting in terms of the weak nexus between each component of ESG and tourism sector's performance.

Simple population models

2019 ◽  
pp. 21-53
Author(s):  
Louis W. Botsford ◽  
J. Wilson White ◽  
Alan Hastings
Keyword(s):  
Continuous Time ◽  
Population Model ◽  
Linear Models ◽  
Nonlinear Models ◽  
Single Variable ◽  
Fibonacci Series ◽  
Basic Concepts ◽  
Dependent Growth ◽  
Geometric Growth ◽  
Simple Models

This chapter introduces basic concepts in population modeling that will be applied throughout the book. It begins with the oldest example of a population model, the rabbit problem, which was described by Leonardo of Pisa (“Fibonacci”) and whose solution is the Fibonacci series. The chapter then explores what is known about simple models of populations (i.e. those with a single variable such as abundance or biomass). The two major classes are: (1) linear models of exponential (or geometric) growth and (2) models of logistic, density-dependent growth. It covers both discrete time and continuous time versions of each of these. These simple models are then used to illustrate several different population dynamic concepts: dynamic stability, linearizing nonlinear models, calculation of probabilities of extinction, and management of sustainable fisheries. Each of these concepts is discussed further in later chapters, with more complete models.

scholarly journals Oscillatory behavior of two nonlinear microbial models of soil carbon decomposition

2014 ◽  
Vol 11 (7) ◽  
pp. 1817-1831 ◽  
Author(s):  
Y. P. Wang ◽  
B. C. Chen ◽  
W. R. Wieder ◽  
M. Leite ◽  
B. E. Medlyn ◽  
...  
Keyword(s):  
Stability Analysis ◽  
Microbial Biomass ◽  
Soil Carbon ◽  
Nonlinear Model ◽  
Linear Models ◽  
Nonlinear Models ◽  
Oscillatory Behavior ◽  
Carbon Pools ◽  
Carbon Decomposition ◽  
Pool Model

Abstract. A number of nonlinear models have recently been proposed for simulating soil carbon decomposition. Their predictions of soil carbon responses to fresh litter input and warming differ significantly from conventional linear models. Using both stability analysis and numerical simulations, we showed that two of those nonlinear models (a two-pool model and a three-pool model) exhibit damped oscillatory responses to small perturbations. Stability analysis showed the frequency of oscillation is proportional to √(ϵ−1−1) Ks/Vs in the two-pool model, and to √(ϵ−1−1) Kl/Vl in the three-pool model, where ϵ is microbial growth efficiency, Ks and Kl are the half saturation constants of soil and litter carbon, respectively, and /Vs and /Vl are the maximal rates of carbon decomposition per unit of microbial biomass for soil and litter carbon, respectively. For both models, the oscillation has a period of between 5 and 15 years depending on other parameter values, and has smaller amplitude at soil temperatures between 0 and 15 °C. In addition, the equilibrium pool sizes of litter or soil carbon are insensitive to carbon inputs in the nonlinear model, but are proportional to carbon input in the conventional linear model. Under warming, the microbial biomass and litter carbon pools simulated by the nonlinear models can increase or decrease, depending whether ϵ varies with temperature. In contrast, the conventional linear models always simulate a decrease in both microbial and litter carbon pools with warming. Based on the evidence available, we concluded that the oscillatory behavior and insensitivity of soil carbon to carbon input are notable features in these nonlinear models that are somewhat unrealistic. We recommend that a better model for capturing the soil carbon dynamics over decadal to centennial timescales would combine the sensitivity of the conventional models to carbon influx with the flexible response to warming of the nonlinear model.

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