scholarly journals Estimation Under Mode Effects and Proxy Surveys, Accounting for Non-ignorable Nonresponse

Sankhya A ◽  
2021 ◽  
Author(s):  
Danny Pfeffermann ◽  
Arie Preminger
Keyword(s):  
Measurement Errors ◽  
Survey Sampling ◽  
Household Surveys ◽  
Personal Interview ◽  
Simulation Experiments ◽  
Mode Effects ◽  
Sample Data ◽  
Point Estimators ◽  
Internet Telephone ◽  
True Population

AbstractWe propose a new, model-based methodology to address two major problems in survey sampling: The first problem is known as mode effects, under which responses of sampled units possibly depend on the mode of response, whether by internet, telephone, personal interview, etc. The second problem is of proxy surveys, whereby sampled units respond not only about themselves but also for other sampled. For example, in many familiar household surveys, one member of the household provides information for all other members, possibly with measurement errors. Ignoring the existence of mode effects and/or possible measurement errors in proxy surveys could result in possible bias in point estimators and subsequent inference. Our approach accounts also for nonignorable nonresponse. We illustrate the proposed methodology by use of simulation experiments and real sample data, with known true population values.

scholarly journals Image detection method of drill cuttings

2019 ◽  
Vol 17 (2) ◽  
pp. 249-257
Author(s):  
Long Guo ◽  
Jing Qian ◽  
Jiangping Si ◽  
Shuxin Yao
Keyword(s):  
Detection Method ◽  
Rock Physics ◽  
Ct Scanner ◽  
Simulation Experiments ◽  
Training Models ◽  
Core Technology ◽  
Sample Data ◽  
Digital Core ◽  
Scanned Image ◽  
Drilling Time

Abstract With the development of digital core technology, numerical simulation experiments have been used by oilfield geologists and geophysicists in rock physics research. However, conducting numerical experiments, such as fluid mechanic simulations, in uncored parts of wells for unavailable digital models to calculate permeability is still impossible. The algorithm filters a large number of Computed Tomography (CT) scanned cutting images and detects whether they can be used to construct a digital core model. This workflow can be applied when coring fails or is too costly. It also provides as much continuous sample data as possible. Conventional methods of acquiring digital cores require coring after drilling and waiting for several months. The method we present here is the innovation of acquiring digital cores in drilling based on a fast CT scanner for a well site. In the future, obtaining petrophysical parameters in uncored strata may be necessary during drilling time. When sandstone is crushed into debris, most of its matrix grains are usually not destroyed. Instead, they are dispersed in the cuttings as loose sand grains. Some original sandstone grains are well connected by cement, and thus may be kept intact as agglomerated grains. These agglomerated grains are selected by collecting a large number of scanned slices. The artificially broken core samples are used for machine learning, and these training models are subsequently used to determine whether the scanned image can be extracted to unspoiled and cemented grains.

Quantifying Energy Consumption in Household Surveys: An Alternative Device-based Accounting Approach

Field Methods ◽  
2020 ◽  
Vol 32 (2) ◽  
pp. 213-232 ◽  
Author(s):  
Shimei Wu ◽  
Xinye Zheng ◽  
Jin Guo ◽  
Chuan-Zhong Li ◽  
Chu Wei
Keyword(s):  
Developing Countries ◽  
Energy Consumption ◽  
Rural Areas ◽  
Measurement Errors ◽  
Household Survey ◽  
Daily Routine ◽  
Household Surveys ◽  
Household Energy ◽  
Consumption Data ◽  
Structured Questionnaire

The exercise of quantifying the energy consumption data assembled through household surveys, either by the recall-based approach or the meter-based approach, remains a challenging task, especially in rural areas of developing countries. In this article, we propose a device-based bottom-up accounting method for estimating household energy consumption. This method provides microlevel disaggregated estimates at the intensive margin and documents other difficult-to-measure energy consumption such as biomass at the extensive margin. Even though measurement errors of the household survey might still exist, the structured questionnaire of daily routine behavior questions should greatly alleviate the problem. The new method supplements the existing household energy statistical system, improves its flexibility, and is particularly applicable in developing countries and/or rural areas. We apply the method to a Chinese rural household survey and discuss its differences and similarities with the conventional methods.

CONJUGATE AND NATURAL GRADIENT RULES FOR BYY HARMONY LEARNING ON GAUSSIAN MIXTURE WITH AUTOMATED MODEL SELECTION

2005 ◽  
Vol 19 (05) ◽  
pp. 701-713 ◽  
Author(s):  
JINWEN MA ◽  
BIN GAO ◽  
YANG WANG ◽  
QIANSHENG CHENG
Keyword(s):  
Model Selection ◽  
Learning Theory ◽  
Gaussian Mixture ◽  
Parameter Learning ◽  
Natural Gradient ◽  
Simulation Experiments ◽  
Sample Data

Under the Bayesian Ying–Yang (BYY) harmony learning theory, a harmony function has been developed on a BI-directional architecture of the BYY system for Gaussian mixture with an important feature that, via its maximization through a general gradient rule, a model selection can be made automatically during parameter learning on a set of sample data from a Gaussian mixture. This paper further proposes the conjugate and natural gradient rules to efficiently implement the maximization of the harmony function, i.e. the BYY harmony learning, on Gaussian mixture. It is demonstrated by simulation experiments that these two new gradient rules not only work well, but also converge more quickly than the general gradient ones.

Lights, Camera … Income! Illuminating the National Accounts-Household Surveys Debate *

2016 ◽  
Vol 131 (2) ◽  
pp. 579-631 ◽  
Author(s):  
Maxim Pinkovskiy ◽  
Xavier Sala-i-Martin
Keyword(s):  
Measurement Errors ◽  
Null Hypothesis ◽  
Household Survey ◽  
Gdp Per Capita ◽  
Household Surveys ◽  
National Accounts ◽  
Optimal Weight ◽  
Nighttime Lights ◽  
World Poverty ◽  
Per Capita

AbstractGDP per capita and household survey means present conflicting pictures of the rate of economic development in emerging countries. One of the areas in which the national accounts–household surveys debate is key is the measurement of developing world poverty. We propose a data-driven method to assess the relative quality of GDP per capita and survey means by comparing them to the evolution of satellite-recorded nighttime lights. Our main assumption, which is robust to a variety of specification checks, is that the measurement error in nighttime lights is unrelated to the measurement errors in either national accounts or survey means. We obtain estimates of weights on national accounts and survey means in an optimal proxy for true income; these weights are very large for national accounts and very modest for survey means. We conclusively reject the null hypothesis that the optimal weight on surveys is greater than the optimal weight on national accounts, and we generally fail to reject the null hypothesis that the optimal weight on surveys is zero. Additionally, we provide evidence that national accounts are good indicators of desirable outcomes for the poor (such as longer life expectancy, better education and access to safe water), and we show that surveys appear to perform worse in developing countries that are richer and that are growing faster. Therefore, we interpret our results as providing support for estimates of world poverty that are based on national accounts.

scholarly journals Research on Artificial Intelligence Classification and Statistical Methods of Financial Data in Smart Cities

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Xuezhong Fu
Keyword(s):  
Artificial Intelligence ◽  
Statistical Methods ◽  
Smart Cities ◽  
Financial Data ◽  
Multidimensional Data ◽  
Data Mining Algorithm ◽  
Data Simulation ◽  
Simulation Experiments ◽  
Sample Data ◽  
Missing Variables

In order to improve the effect of financial data classification and extract effective information from financial data, this paper improves the data mining algorithm, uses linear combination of principal components to represent missing variables, and performs dimensionality reduction processing on multidimensional data. In order to achieve the standardization of sample data, this paper standardizes the data and combines statistical methods to build an intelligent financial data processing model. In addition, starting from the actual situation, this paper proposes the artificial intelligence classification and statistical methods of financial data in smart cities and designs data simulation experiments to conduct experimental analysis on the methods proposed in this paper. From the experimental results, the artificial intelligence classification and statistical method of financial data in smart cities proposed in this paper can play an important role in the statistical analysis of financial data.

scholarly journals A new high-precision borehole-temperature logging system used at GISP2, Greenland, and Taylor Dome, Antarctica

1996 ◽  
Vol 42 (142) ◽  
pp. 576-584 ◽  
Author(s):  
Gary D. Clow ◽  
Richard W. Saltus ◽  
Edwin D. Waddington
Keyword(s):  
High Precision ◽  
Measurement Errors ◽  
The United States ◽  
United States Geological Survey ◽  
Polar Regions ◽  
Present Sample ◽  
Sample Data ◽  
Fluid Convection ◽  
Borehole Temperature ◽  
Pressure Changes

AbstractWe describe a high-precision (0.1–1.0 mK) borehole-temperature (BT) logging system developed at the United States Geological Survey (USGS) for use in remote polar regions. We discuss calibration, operational and data-processing procedures, and present an analysis of the measurement errors. The system is modular to facilitate calibration procedures and field repairs. By interchanging logging cables and temperature sensors, measurements can be made in either shallow air-filled boreholes or liquid-filled holes up to 7 km deep. Data can be acquired in either incremental or continuous-logging modes. The precision of data collected by the new logging system is high enough to detect and quantify various thermal effects at the milli-Kelvin level. To illustrate this capability, we present sample data from the 3 km deep borehole at GISP2, Greenland, and from a 130 m deep air-filled hole at Taylor Dome, Antarctica. The precision of the processed GISP2 continuous temperature logs is 0.25–0.34 mK, while the accuracy is estimated to be 4.5 mK. The effects of fluid convection and the dissipation of the thermal disturbance caused by drilling the borehole are clearly visible in the data. The precision of the incremental Taylor Dome measurements varies from 0.11 to 0.32 mK. depending on the wind strength during the experiments. With this precision, we found that temperature fluctuations and multi-hour trends in the BT measurements correlate well with atmospheric-pressure changes.

scholarly journals A new high-precision borehole-temperature logging system used at GISP2, Greenland, and Taylor Dome, Antarctica

1996 ◽  
Vol 42 (142) ◽  
pp. 576-584
Author(s):  
Gary D. Clow ◽  
Richard W. Saltus ◽  
Edwin D. Waddington
Keyword(s):  
High Precision ◽  
Measurement Errors ◽  
The United States ◽  
United States Geological Survey ◽  
Polar Regions ◽  
Present Sample ◽  
Sample Data ◽  
Fluid Convection ◽  
Borehole Temperature ◽  
Pressure Changes

AbstractWe describe a high-precision (0.1–1.0 mK) borehole-temperature (BT) logging system developed at the United States Geological Survey (USGS) for use in remote polar regions. We discuss calibration, operational and data-processing procedures, and present an analysis of the measurement errors. The system is modular to facilitate calibration procedures and field repairs. By interchanging logging cables and temperature sensors, measurements can be made in either shallow air-filled boreholes or liquid-filled holes up to 7 km deep. Data can be acquired in either incremental or continuous-logging modes. The precision of data collected by the new logging system is high enough to detect and quantify various thermal effects at the milli-Kelvin level. To illustrate this capability, we present sample data from the 3 km deep borehole at GISP2, Greenland, and from a 130 m deep air-filled hole at Taylor Dome, Antarctica. The precision of the processed GISP2 continuous temperature logs is 0.25–0.34 mK, while the accuracy is estimated to be 4.5 mK. The effects of fluid convection and the dissipation of the thermal disturbance caused by drilling the borehole are clearly visible in the data. The precision of the incremental Taylor Dome measurements varies from 0.11 to 0.32 mK. depending on the wind strength during the experiments. With this precision, we found that temperature fluctuations and multi-hour trends in the BT measurements correlate well with atmospheric-pressure changes.

scholarly journals The Error Performances of Some Residual Optimization Methods

2016 ◽  
Vol 24 (2) ◽  
pp. 191
Author(s):  
Setyono -
Keyword(s):  
Optimization Methods ◽  
Optimization Method ◽  
Small Error ◽  
Regression Coefficients ◽  
Error Performance ◽  
Simulation Experiments ◽  
Absolute Residual ◽  
Sample Data ◽  
Good Statistic ◽  
Error Optimization

A good statistic is unbiased and efficient. Because the encountered data in practice is a sample data with a certain size, the required statistic is not unbiased statistic, but statistic that has small error. When the encountered data is only a sample data, then that can be done is not error optimization but is residual optimization. This study aims to examine the error performance of three methods of residual optimization, they are by minimizing the maximum of absolute residual (MLAD), by minimizing the sum of absolute residual (LAD), and by minimizing the sum of squared residual (LS). Research results using simulation experiments showed that if the data have uniform distribution, the residual optimization method by minimizing maximum of absolute residual get the smallest error. Meanwhile, residual optimization method by minimizing the sum of squared residual get the smallest error when the data have normal or exponential distribution. This property is true when statistics to be estimated are measure of central tendency, regression coefficients, and the response of regression.

A Novel Method to Simultaneously Identify Temperature-Dependent Thermal Properties and Verification

2019 ◽  
Vol 12 (4) ◽  
Author(s):  
Weizhen Pan ◽  
Fajun Yi ◽  
Lijun Zhuo ◽  
Songhe Meng
Keyword(s):  
Thermal Properties ◽  
Measurement Errors ◽  
Temperature Data ◽  
Random Errors ◽  
Temperature Dependent ◽  
Simulation Experiments ◽  
Marquardt Method ◽  
Inverse Heat Transfer ◽  
Novel Method ◽  
Transfer Problems

Abstract A novel method for the identification of thermal conductivity and specific heat capacity simultaneously by solving inverse heat transfer problems (IHTPs) is proposed. The present method uses a new iterative format of the Levenberg–Marquardt method (LMM) and guarantees global convergence by implementing the subsection identification method. Both simulation and real experiments are conducted to prove the validity and practicability of the proposed method. The thermal properties in simulation and real experiments are identified, respectively, by the proposed method. In the simulation experiments, random errors are added into temperature data to survey the effect of measurement errors on the identification; and the deviations of the results are also compared to that in a published literature to show the superiority of the proposed method. The numerical results illustrate that the identification is accurate and stable. And the identification results of the real experiment are compared with measured ones, proving the practicability of the method.

scholarly journals Predicting population gene frequency from sample data

2004 ◽  
Vol 78 (1) ◽  
pp. 03-11 ◽  
Author(s):  
R. M. Lewis ◽  
B. Grundy ◽  
L. A. Kuehn
Keyword(s):  
Random Sampling ◽  
Gene Frequency ◽  
Sampling Strategy ◽  
Sampling Strategies ◽  
Statistical Sampling ◽  
Effective Strategies ◽  
Sample Data ◽  
Potential Risks ◽  
Sampling Event ◽  
True Population

AbstractWith an increase in the number of candidate genes for important traits in livestock, effective strategies for incorporating such genes into selection programmes are increasingly important. Those strategies in part depend on the frequency of a favoured allele in a population. Since comprehensive genotyping of a population is seldom possible, we investigate the consequences of sampling strategies on the reliability of the gene frequency estimate for a bi-allelic locus. Even within a subpopulation or line, often only a proportion of individuals will be genotype tested. However, through segregation analysis, probable genotypes can be assigned to individuals that themselves were not tested, using known genotypes on relatives and a starting (presumed) gene frequency. The value of these probable genotypes in estimation of gene frequency was considered. A subpopulation or line was stochastically simulated and sampled at random, over a cluster of years or by favouring a particular genotype. Line was simulated (replicated) 1000 times. The reliability of gene frequency estimates depended on the sampling strategy used. With random sampling, even when a small proportion of a line was genotyped (0·10), the gene frequency of the population was well estimated from the across-line mean. When information on probable genotypes on untested individuals was combined with known genotypes, the between-line variance in gene frequency was estimated well; including probable genotypes overcame problems of statistical sampling. When the sampling strategy favoured a particular genotype, unsurprisingly the estimate of gene frequency was biased towards the allele favoured. In using probable genotypes the bias was lessened but the estimate of gene frequency still reflected the sampling strategy rather than the true population frequency. When sampling was confined to a few clustered years, the estimation of gene frequency was biased for those generations preceding the sampling event, particularly when the presumed starting gene frequency differed from the true population gene frequency. The potential risks of basing inferences about a population from a potentially biased sample are discussed.

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