Simple Adjustment Method Research Articles

Adjusting for bias in the mean for primary and secondary outcomes when trials are in sequence.

When designing a clinical trial, one key aspect of the design is the sample size calculation. The sample size calculation tends to rely on a target or expected difference. The expected difference can be based on the observed data from previous studies, which results in bias. It has been reported that large treatment effects observed in trials are often not replicated in subsequent trials. If these values are used to design subsequent studies, the sample sizes may be biased which results in an unethical study. Regression to the mean (RTM) is one explanation for this. If only health technologies which meet a particular continuation criterion (such as in the first study) are progressed to a second confirmatory trial, it is highly likely that the observed effect in the second trial will be lower than that observed in the first trial. It will be shown how when moving from one trial to the next, a truncated normal distribution is inherently imposed on the first study. This results in a lower observed effect size in the second trial. A simple adjustment method is proposed based on the mathematical properties of the truncated normal distribution. This adjustment method was confirmed using simulations in R and compared with other previous adjustments. The method can be applied to the observed effect in a trial, which is being used in the design of a second confirmatory trial, resulting in a more stable estimate for the 'true' treatment effect. The adjustment accounts for the bias in the primary and secondary endpoints in the first trial with the bias being affected by the power of that study. Tables of results have been provided to aid implementation, along with a worked example. In summary, there is a bias introduced when the point estimate from one trial is used to assist the design of a second trial. It is recommended that any observed point estimates be used with caution and the adjustment method developed in this article be implemented to significantly reduce this bias.

Relation between hearing abilities and preferred playback settings for speech perception in complex listening conditions

Objective This study investigated if individual preferences with respect to the trade-off between a good signal-to-noise ratio and a distortion-free speech target were stable across different masking conditions and if simple adjustment methods could be used to identify subjects as either “noise haters” or “distortions haters”. Design In each masking condition, subjects could adjust the target speech level according to their preferences by employing (i) linear gain or gain at the cost of (ii) clipping distortions or (iii) compression distortions. The comparison of these processing conditions allowed investigating the preferred trade-off between distortions and noise disturbance. Study sample Thirty subjects differing widely in hearing status (normal-hearing to moderately impaired) and age (23–85 years). Results High test–retest stability of individual preferences was found for all modification schemes. The preference adjustments suggested that subjects could be consistently categorised along a scale from “noise haters” to “distortion haters”, and this preference trait remained stable through all maskers, spatial conditions, and types of distortions. Conclusions Employing quick self-adjustment to collect listening preferences in complex listening conditions revealed a stable preference trait along the “noise vs. distortions” tolerance dimension. This could potentially help in fitting modern hearing aid algorithms to the individual user.

Estimating high-dimensional covariance and precision matrices under general missing dependence

A sample covariance matrix S of completely observed data is the key statistic in a large variety of multivariate statistical procedures, such as structured covariance/precision matrix estimation, principal component analysis, and testing of equality of mean vectors. However, when the data are partially observed, the sample covariance matrix from the available data is biased and does not provide valid multivariate procedures. To correct the bias, a simple adjustment method called inverse probability weighting (IPW) has been used in previous research, yielding the IPW estimator. The estimator can play the role of S in the missing data context, thus replacing S in off-the-shelf multivariate procedures such as the graphical lasso algorithm. However, theoretical properties (e.g. concentration) of the IPW estimator have been only established in earlier work under very simple missing structures; every variable of each sample is independently subject to missingness with equal probability. We investigate the deviation of the IPW estimator when observations are partially observed under general missing dependency. We prove the optimal convergence rate Op( logp∕n) of the IPW estimator based on the element-wise maximum norm, even when two unrealistic assumptions (known mean and/or missing probabilities) frequently assumed to be known in the past work are relaxed. The optimal rate is especially crucial in estimating a precision matrix, because of the “meta-theorem” [26] that claims the rate of the IPW estimator governs that of the resulting precision matrix estimator. In the simulation study, we discuss one of practically important issues, non-positive semi-definiteness of the IPW estimator, and compare the estimator with imputation methods.

Dynamics of Laser-Charged UAVs: A Battery Perspective

In this article, we aim to sustain unmanned aerial vehicle (UAV)-based missions for longer periods of times through different techniques. First, we consider on-the-mission UAV recharging by a low-power laser source (below 1 kW). In order to achieve the maximal energy gain from the low-power laser source, we propose an operational compromise, which consists of the UAV resting over buildings with cleared line of sight to the laser source. Second, to provide a precise energy consumption/harvesting estimation at the UAV, we investigate the latter's dynamics in a mission environment. Indeed, we study the UAV's battery dynamics by leveraging the electrical models for motors and battery. Subsequently, using these models, the path planning problem in a particular Internet-of-Things-based usecase is revisited from the battery perspective. The objective is to extend the UAV's operation time using both laser-charging and accurate battery level estimation. Through a graph theory approach, the problem is solved optimally, and compared to benchmark trajectory approaches. Numerical results demonstrate the efficiency of this novel battery perspective for all path planning approaches. In contrast, we found that the energy perspective is very conservative and does not exploit optimally the available energy resources. Nevertheless, we propose a simple adjustment method to correct the energy perspective, by carefully evaluating the energy as a function of the UAV motion regimes. Finally, the impact of several parameters, such as turbulence and distance to charging source, is studied.

A Switchable Sound Tunnel by Using an Acoustic Metasurface

An acoustic metasurface (AM) is proposed to achieve a sound tunnel with switchable functionalities of asymmetric transmission and bidirectional sound isolation, which is composed of an array of steel containers filled with two inert gases, argon and xenon. Acoustic asymmetric transmission can be observed when the AM is placed vertically. The functionality of the tunnel can be switched to bidirectional sound isolation by simply rotating the AM without changing the physical structure of the device. The proposed switchable tunnel has broadband multi-functional response with a simple adjustment method, showing potential for bidirectional sound isolation and sound sensing.

Auto-optimization control of hybrid ecological refrigeration system

The optimization problem of the two-stage hybrid compression-sorption refrigeration control system is presented. The idea is an eco-friendly energy conversion, powered by the waste heat or renewable energy. Eco-friendly means that using only natural energy carriers H2O and CO2 are used. The goal of the optimization is maximization of the renewable energy usage and increasing of the system COP, by minimization of the power consumption. The optimization problem is complex due to the large amount of control parameters. In the programmable logic controller (PLC) a set of functions is implemented. As a result the consumption of all kinds of energy supplied to the system in the different modes of operation may be calculated online. This can be applied as the control function for optimal usage of the available heat sources, while maintaining the temperature in the refrigerator compartment at desired level. Similar method of process optimization in the last years was introduced for the energy consumption and cost reduction in the operation of industrial and home appliances. In larger industrial scale more energy savings are possible using this approach. The work of entire refrigeration system can be based on this idea, reducing the consumption of electricity used to drive compressors, fans and pumps. Each step of the optimization has to consider process disturbances and external condition changes, therefore the system control becomes a complex issue. Number of optimization methods may be found in the literature. However in this case the application of the simple adjustment method based on the selected variables, control parameters, with dependence between them was proposed. This approach has been called auto or self-optimization. In this method the nominal value is set, but some possibility of losses are accepted. This type of optimization is used with single or several linear variables depending on the measured value in the system, which are usually controlled by PI or PID controllers.

Identifying changing patterns of reservoir operating rules under various inflow alteration scenarios

Operating rules are important in the long-term operation of reservoirs for its capability of coping with inflow uncertainty. The characteristics of inflow vary as a result of climate change and human activities, and using stationary operating rules would lead to inefficient reservoir operation. This study focuses on identifying changing patterns of operating rules under various inflow alteration scenarios. Two simulation methods, the simple adjustment method (SAM) and the stochastic reconstruction method (SRM), are used to generate three inflow alteration scenarios: shifts of mean, coefficient of variation (CV), and seasonality. A deterministic reservoir optimization model is established and then resolved using discrete differential dynamic programming algorithm. Finally, the operating rules under each scenario are derived using the linear fitting method. China's Three Gorges Reservoir is used as a case study. The results show that the SAM and SRM produce similar operating rules, which are sensitive to inflow changes during refill and drawdown periods. It is shown that (1) the increase (decrease) of inflow mean changes the operating rules, resulting in the increase (decrease) of the water releases while the shift of CV has little impact on operating rules; (2) the seasonality changes operating rules in opposite directions during refill and drawdown periods; (3) the changing patterns of operating rules would be superimposed by the superposition of various inflow alteration scenarios whereas the effects might be not obvious. These findings are helpful for adaptive operation of reservoirs under changing environment.

Global Equities Underperformance Disguised asOutperformance

Investors exactly replicating the MSCI World Index can show an apparent outperformance versus MSCI World (net) of up to 50 basis points per annum, on average, depending on their country of residence. This apparent outperformance is entirely due to the different dividend-withholding tax rates in the benchmark versus what is actually applicable to the investor—and not due to manager skill. It arises because the withholding tax assumptions used in the construction of the benchmark are significantly higher than those actually faced by investors, due to the availability of double-taxation treaties. Investors need to be aware of this effect to ensure they don’t mistakenly attribute this apparent outperformance to manager skill. In this article, the authors calculate and explain this apparent outperformance for investors domiciled in the United States, Australia, Germany, Japan, Switzerland, or the United Kingdom and then propose a simple adjustment method to the net benchmark to better represent the actual return to investors based on their location and the actual withholding taxes incurred.

Analysis of boundedness and convergence of online gradient method for two-layer feedforward neural networks.

This paper presents a theoretical boundedness and convergence analysis of online gradient method for the training of two-layer feedforward neural networks. The well-known linear difference equation is extended to apply to the general case of linear or nonlinear activation functions. Based on this extended difference equation, we investigate the boundedness and convergence of the parameter sequence of concern, which is trained by finite training samples with a constant learning rate. We show that the uniform upper bound of the parameter sequence, which is very important in the training procedure, is the solution of an inequality regarding the bound. It is further verified that, for the case of linear activation function, a solution always exists and, moreover, the parameter sequence can be uniformly upper bounded, while for the case of nonlinear activation function, some simple adjustment methods on the training set or the activation function can be derived to improve the boundedness property. Then, for the convergence analysis, it is shown that the parameter sequence can converge into a zone around an optimal solution at which the error function attains its global minimum, where the size of the zone is associated with the learning rate. Particularly, for the case of perfect modeling, a strong global convergence result, where the parameter sequence can always converge to an optimal solution, is proved.

Adjustment method for microarray data generated using two-cycle RNA labeling protocol

BackgroundMicroarray technology is widely utilized for monitoring the expression changes of thousands of genes simultaneously. However, the requirement of relatively large amount of RNA for labeling and hybridization makes it difficult to perform microarray experiments with limited biological materials, thus leads to the development of many methods for preparing and amplifying mRNA. It is addressed that amplification methods usually bring bias, which may strongly hamper the following interpretation of the results. A big challenge is how to correct for the bias before further analysis.ResultsIn this article, we observed the bias in rice gene expression microarray data generated with the Affymetrix one-cycle, two-cycle RNA labeling protocols, followed by validation with Real Time PCR. Based on these data, we proposed a statistical framework to model the processes of mRNA two-cycle linear amplification, and established a linear model for probe level correction. Maximum Likelihood Estimation (MLE) was applied to perform robust estimation of the Retaining Rate for each probe. After bias correction, some known pre-processing methods, such as PDNN, could be combined to finish preprocessing. Then, we evaluated our model and the results suggest that our model can effectively increase the quality of the microarray raw data: (i) Decrease the Coefficient of Variation for PM intensities of probe sets; (ii) Distinguish the microarray samples of five stages for rice stamen development more clearly; (iii) Improve the correlation coefficients among stamen microarray samples. We also discussed the necessity of model adjustment by comparing with another simple adjustment method.ConclusionWe conclude that the adjustment model is necessary and could effectively increase the quality of estimation for gene expression from the microarray raw data.

Sentiment Analysis of Korean Using Effective Linguistic Features and Adjustment of Word Senses

This paper introduces anew linguistic-focused approach for sentiment analysis (SA) of Korean. In order to overcome shortcomings of previous works that focused mainly on statistical methods, we made effective use of various linguistic features reflecting the nature of Korean. These features include contextual shifters, modal affixes, and the morphological dependency of chunk structures. Moreover, in order to eschew possible confusion caused by ambiguous words and to improve the results of SA, we also proposed simple adjustment methods of word senses using KOLON ontology mapping information. Through experiments we contend that effective use of linguistic features and ontological information can improve the results of sentiment analysis of Korean. (Seoul National University)

Evaluating regional climate model estimates against site-specific observed data in the UK

This paper compares precipitation, maximum and minimum air temperature and solar radiation estimates from the Hadley Centre's HadRM3 regional climate model (RCM), (50×50 km grid cells), with observed data from 15 meteorological station in the UK, for the period 1960-90. The aim was to investigate how well the HadRM3 is able to represent weather characteristics for a historical period (hindcast) for which validation data exist. The rationale was to determine if the HadRM3 data contain systematic errors and to investigate how suitable the data are for use in climate change impact studies at particular locations. Comparing modelled and observed data helps assess and quantify the uncertainty introduced to climate impact studies. The results show that the model performs very well for some locations and weather variable combinations, but poorly for others. Maximum temperature estimations are generally good, but minimum temperature is overestimated and extreme cold events are not represented well. For precipitation, the model produces too many small events leading to a serious under estimation of the number of dry days (zero precipitation), whilst also over- or underestimating the mean annual total. Estimates represent well the temporal distribution of precipitation events. The model systematically over-estimates solar radiation, but does produce good quality estimates at some locations. It is concluded that the HadRM3 data are unsuitable for detailed (i.e. daily time step simulation model based) site-specific impacts studies in their current form. However, the close similarity between modelled and observed data for the historical case raises the potential for using simple adjustment methods and applying these to future projection data.

A method to defuzzify the fuzzy number: transportation problem application

In many problems of transportation engineering and planning, we encounter situations, in which the observed or derived values of the variables are approximate, yet the variables themselves must satisfy a set of rigid relationships dictated by physical principle. When the observed values do not satisfy the relationships, each value is adjusted until they satisfy the relationship. We propose a simple adjustment method that finds the most appropriate set of crisp numbers. The method assumes that each observed value is an approximate number (or a fuzzy number) and the true value is found in the support of the membership function. For each of many possible sets of values that satisfy the relationships, the lowest membership grade is checked and the set whose lowest membership grade is the highest is chosen as the best set of values for the problem. This process is performed using the fuzzy linear programming method. The paper presents the model, the computational process and applications.

Household welfare measurement and the pricing of basic services

Employing total consumption as a household-level welfare indicator with which to measure poverty has often been criticized because it is felt that such an indicator does not account for differing access to, and cost of, publicly provided services. This paper discusses when and how adjustments can be made to expenditures derived from household surveys so as to reflect the consumption of basic services. Markets which are subsidized, rationed and subject to increasing marginal tariff pricing are examined and simple adjustment methods are discussed. Using Ecuador to illustrate the methods, the paper shows that incorporating adjustments in markets for basic services can significantly alter poverty estimates. Including such adjustments into a comprehensive measure of welfare can therefore be important and can also contribute to the wider acceptance and use of consumption as a welfare indicator guiding public policy development. Copyright © 2000 John Wiley & Sons, Ltd.

Household welfare measurement and the pricing of basic services

The authors discuss when and how to adjust expenditures derived from household surveys to reflect the consumption of basic services. They discuss simple adjustment methods for markets that are subsidized, rationed, or subject to increasing marginal tariff pricing. Using Ecuador as an example, they show how incorporating adjustments in markets for water, electricity, and cooking gas can significantly alter estimates of poverty and are therefore important to comprehensive measure of welfare. For Ecuador, adjustments must be made for water, for example, because the nonpoor urban population often has access to subsidized public water and the poor depend on the private market; adjustments must be made for electricity because increasing marginal tariff rates lead to different prices per kilowatt-hour (kwh). Adjustments need not be made for cooking gas, which is highly subsidized in Ecuador, because the amount consumers use is not rationed. The authors compare the sensitivity of poverty indicators and the poverty profile in Ecuador to adjustments in nominal expenditures for basic services in Ecuador. The poverty indicators (headcount and the poverty gap for extreme poverty) showed changes that were statistically significant. The results dramatize how important it is to carefully analyze markets for basic services when deriving welfare measures from household surveys. Such adjustments, by improving the measure of welfare, can also encourage wider acceptance and use of consumption as welfare indicator and a guide for developing public policy.

Reliabilities of alternate measures of contrast sensitivity functions.

In four studies, the comparability of several measures of contrast sensitivity functions (CSF's) as well as their individual test-retest reliabilities were determined with observers drawn from a college population. The relation between CSF scores obtained by different tests or by different psychophysical procedures with the same test was consistently low and frequently nonexistent. These low intertest reliabilities were found to be due to the lack of reliability of some of the individual tests. In particular, with both stationary and flickering gratings, the von Bekesy tracking procedure produced unacceptable variations in contrast estimates. Fortunately, the reliabilities of other procedures, particularly the simple adjustment method, were much higher (greater than 0.70). CSF scores obtained from the wallchart method exhibited insufficient variability among observers in the college sample to permit similar reliability estimates to be determined. Implications of the test characteristics are discussed for users of the CSF.