Simple Hypothesis Testing Research Articles

The Deep Determinants of the Middle-Income Trap

The fundamental, underlying factors of development are often neglected when analyzing the question why countries experience a growth slowdown at the middle-income range. Although these so-called ‘deep determinants’ such as geography and institutions have been found to be decisive for the break out of stagnation and for explaining cross-country income differences by many empirical studies, so far, very little has been done to examine to which extent they are also crucial at more subtle stages of economic development. Our paper aims to contribute to close this gap by focusing on the phenomenon of the middle-income trap (MIT) which has reached increasing attention in the last 15 years. In particular, we are interested in whether the deep determinants have positive or negative impacts on the possibility of a country to experience a prolonged stay within the middle-income range. We focus especially on exogenous variables to avoid endogeneity/reverse causality problems. By using simple statistical hypothesis testing, we show that not all findings of the deep determinants literature can be easily transferred to the MIT phenomenon, especially regarding institutional variables. This may raise the question whether we need new deep determinants of growth for the MIT or at least a modified version taking into account the specific circumstances and characteristics of middle-income countries.

Finite-sample and asymptotic sign-based tests for parameters of non-linear quantile regression with Markov noise

One of the most noticeable features of sign-based statistical procedures is an opportunity to build an exact test for simple hypothesis testing of parameters in a regression model. In this article, we expanded a sing-based approach to the nonlinear case with dependent noise. The examined model is a multi-quantile regression, which makes it possible to test hypothesis not only of regression parameters, but of noise parameters as well.

Constrained Bayesian Method of Composite Hypotheses Testing: Singularities and Capabilities

The paper deals with the constrained Bayesian Method (CBM) for testing composite hypotheses. It is shown that, similarly to the cases when CBM is optimal for testing simple and multiple hypotheses in parallel and sequential experiments, it keeps the optimal properties at testing composite hypotheses. In particular, it easily, without special efforts, overcomes the Lindley’s paradox arising when testing a simple hypothesis versus a composite one. The CBM is compared with Bayesian test in the classical case and when the a priori probabilities are chosen in a special manner for overcoming the Lindley’s paradox. Superiority of CBM against these tests is demonstrated by simulation. The justice of the theoretical judgment is supported by many computation results of different characteristics of the considered methods.

Prediction of discomfort glare from windows under tropical skies

Discomfort glare is common problem in modern buildings that employ highly-glazed façades. Glare not only has negative impacts on occupant comfort but can also increase energy use in buildings. Yet a robust discomfort glare metric, particularly with regards to daylight, has proven elusive. The Unified Glare Probability (UGP), developed using 493 surveys under clear skies in Brisbane, Australia, shows promise as useful tool for estimating glare from windows. This investigation attempts to validate the UGP using a similarly large number of surveys under different sky conditions, different building types and demographics in the tropical climate of Kuala Lumpur, Malaysia.The study uses a similar methodology to the initial investigation of the UGP, using a combination of luminance mapping with a post-occupancy evaluation (POE) questionnaire on discomfort glare. A total of 341 surveys were collected from six buildings; three green-rated and three regular office buildings. Hypothesis testing of the luminance data collected in these buildings indicate that the ratio of window to background luminances are a more sensitive measure of occupant discomfort than ratios of window to task luminances.The results for the UGP show agreement in predicting discomfort from windows between both data sets using regression analysis. A simple hypothesis test showed discomfort was accurately predicted by the old UGP model for 69% of new surveys. The agreement between the two data sets for window glare enabled them to be combined into one massive data set of 813 surveys to update the UGP. The updated metric uses a logistic curve in place of the original linear transformation of the Unified Glare Rating (UGR) to prevent illogical values of probability being obtained from the metric.

A New Approach to Backtesting and Risk Model Selection

Backtesting risk measures represent a challenge and complex methods are often required. In this paper, we propose a new framework for backtesting that can be applied to every risk measures. We base our approach on the formalization of the concept of level of coverage associated with the risk model. Thus, we propose two simple hypothesis tests based only on results of probability theory without requiring any approximation or simulation. In addition, within this new framework, we introduce a methodology for selecting the best performing model among all the existing alternatives. This proposal adds value to the current state of the art, since, using the traditional loss function approach, any comparison among forecasting outcomes of different risk models appeared to be meaningless.

Physical Layer Authentication Enhancement Using Two-Dimensional Channel Quantization

A novel physical layer authentication enhancement scheme is proposed in this paper by integrating multipath delay characteristics of wireless channels into the channel impulse response (CIR)-based physical layer authentication framework. In order to simplify the decision rule for authentication, a two-dimensional (2-D) quantization method is developed to preprocess the channel variations. More specifically, two one-bit quantizers are used to quantize the temporal channel variations in the dimensions of channel amplitude and path delay, respectively. Under a simple hypothesis testing, a new test statistic is developed based on the sum of outputs of the two quantizers. For performance analysis, false alarm rate (FAR) and probability of detection (PD) are defined based on the developed test statistic, and their closed-form expressions are derived as well. An optimization problem is defined for finding optimal parameters of the proposed scheme based on exhaustive search method. Monte Carlo simulations are utilized to evaluate the performance of the proposed scheme. Compared with other existing method in the literature, the proposed scheme outperforms significantly in spoofing detection.

A new statistical approach to climate change detection and attribution

We propose here a new statistical approach to climate change detection and attribution that is based on additive decomposition and simple hypothesis testing. Most current statistical methods for detection and attribution rely on linear regression models where the observations are regressed onto expected response patterns to different external forcings. These methods do not use physical information provided by climate models regarding the expected response magnitudes to constrain the estimated responses to the forcings. Climate modelling uncertainty is difficult to take into account with regression based methods and is almost never treated explicitly. As an alternative to this approach, our statistical model is only based on the additivity assumption; the proposed method does not regress observations onto expected response patterns. We introduce estimation and testing procedures based on likelihood maximization, and show that climate modelling uncertainty can easily be accounted for. Some discussion is provided on how to practically estimate the climate modelling uncertainty based on an ensemble of opportunity. Our approach is based on the “models are statistically indistinguishable from the truth” paradigm, where the difference between any given model and the truth has the same distribution as the difference between any pair of models, but other choices might also be considered. The properties of this approach are illustrated and discussed based on synthetic data. Lastly, the method is applied to the linear trend in global mean temperature over the period 1951–2010. Consistent with the last IPCC assessment report, we find that most of the observed warming over this period (+0.65 K) is attributable to anthropogenic forcings (+0.67 $$\pm$$ 0.12 K, 90 % confidence range), with a very limited contribution from natural forcings ( $$-0.01\pm 0.02$$ K).

Notes on Statistical Tests for Nonidentically Distributed Observations

We discuss a problem of testing simple statistical hypotheses on the nonidentically distributed observations. A possibility of using the method Monte-Carlo for choosing the critical value in Neyman – Pearson test is pointed out. We propose also an approach permitting to reduce the problem considered to a problem of testing hypotheses on uniformity vs nonuniformity of distributions on the interval (0,1).

Pengaruh Leader Member Exchange Terhadap Kinerja Pegawai

Leaders have the responsibility for managing their human resources. Thus, a good relationship between leaders and their subordinates is needed in order to support the achievement of organizational goals. But in reality, indirectly leaders give higher trust and respect to some of their subordinates. Therefore, the performance of each employee would not be equal to each other. During four semesters of 2013-2014, there has been a fluctuation of the results of the measurement of employee performance of PT PLN (Persero) Distribusi Jawa Barat dan Banten. The decrease in employee performance began to occur in the second semester of 2013, and the more significant decrease occurred in the second semester of 2014. Of course, this performance’s decreasing would give negative impacts for the company. Based on these problems, this study was conducted to determine how the influence of leader-member exchange on employee performance at Distribution Office of PT PLN (Persero) Distribusi Jawa Barat dan Banten. Simple random sampling technique was used to collect data through questionnaires distributed to 123 respondents who are functional employees at the Distribution Office of PT PLN (Persero) Distribusi Jawa Barat dan Banten. The analysis method used in this research is the classical assumption test, descriptive analysis, correlation, simple linear regression and hypothesis testing with SPSS 20. The results showed that Distribution Office of PT PLN (Persero) Distribusi Jawa Barat dan Banten has a high leader-member exchange and good performance. There is a moderate correlation between leader-member exchange and employee performance. Leader-member exchange contributes positively and significantly affect their employee performance of 23,7% while the remaining 76,3% is affected by other factors contribution that is not explained in this research.

Influence analysis of robust Wald-type tests

We consider a robust version of the classical Wald test statistics for testing simple and composite null hypotheses for general parametric models. These test statistics are based on the minimum density power divergence estimators instead of the maximum likelihood estimators. An extensive study of their robustness properties is given though the influence functions as well as the chi-square inflation factors. It is theoretically established that the level and power of these robust tests are stable against outliers, whereas the classical Wald test breaks down. Some numerical examples confirm the validity of the theoretical results.

Variable elimination in nested DEA models: a statistical approach

In this study, a new definition of a relevant variable in a DEA model is proposed for variable selection. The selection procedure is the conventional iterative backward elimination procedure with multiple statistical comparisons. The multiple tests of null hypothesis are reduced to a simple hypothesis test using either the binomial probability or the McNemar test with Bonferroni correction of significant level. From the results based on two simulation populations of moderately and lowly correlated input variables, the proposed procedure using either one of the suggested statistical tests can identify the relevant variables with high accuracy and eliminate the irrelevant variables effectively. In the dataset from a large scale experiment in the US public school education, the reduced model selected by the proposed procedure is shown to be the better approximation of the full model than the ones selected by the Pastor et al. method.

On the GLR and UMP tests in the family with support dependent on the parameter

Some general results about the GLR tests, for testing simple hypothesis versus two-sided hypothesis, in the family with support dependent on the parameter, are obtained. In addition, we show that such GLR tests are equivalent to the UMP tests in the same problems. Moreover, we derive the general form of the UMP tests for testing an interval hypothesis versus two-sided alternative.

On the GLR and UMP tests in the family with support dependent on the parameter

Some general results about the GLR tests, for testing simple hypothesis versus two-sided hypothesis, in the family with support dependent on the parameter, are obtained. In addition, we show that such GLR tests are equivalent to the UMP tests in the same problems. Moreover, we derive the general form of the UMP tests for testing an interval hypothesis versus two-sided alternative.

압축 센싱 기반의 신호 검출 및 추정 방법

Compressive sensing is a new data acquisition method enabling the reconstruction of sparse or compressible signals from a smaller number of measurements than Nyquist rate, as long as the signal is sparse and the measurement is incoherent. In this paper, we consider a simple hypothesis testing in target detection and estimation problems using compressive sensing, where the performance depends on the sparsity level of the signals being detected. We provide theoretical analysis results along with some experiment results.

A Simple Method for Uniform Subvector Inference of Linear Instrumental Variables Models

We propose a procedure for testing simple hypotheses on a subset of the structural parameters in linear instrumental variables models. Our test is valid uniformly over a large class of distributions allowing for identification failure and heteroskedasticity. The large-sample distribution of our test statistic is shown to depend on a key quantity that cannot be consistently estimated. Under our proposed procedure, we construct a confidence set for this key quantity and then maximize, over this confidence set, the appropriate quantile of the large-sample distribution of the test statistic. This maximum is used as the critical value and Bonferroni correction is used to control the overall size of the test. Monte Carlo simulations demonstrate the advantage of our test over the projection method in finite samples.

Pengaruh Motivasi Terhadap Minat Mahasiswa Akuntansi untuk Mengikuti Pendidikan Profesi Akuntansi (PPAK) di Propinsi Lampung

This study aims to analyze and provide empirical evidence about the effect of motivation on student interest in accounting to follow accounting education program. Hypothesis in this research is motivation influence of student interest in accounting to follow accounting education program. The data used in this study is primary data. The primary data used by using questionnaire obtained from 90 respondents is S1 students majoring in accounting at the University of Lampung Province who have taken 120 credits or who was in the seventh semester. Samples were obtained by proportional sampling. The data analysis technique used in this study is a simple regression analysis and hypothesis testing t test using SPSS 18.0 for windows. Results of the study was the hypothesis is accepted as motivation affect the interest of the education of accounting students to follow the accounting profession.

Pengaruh Kinerja Hutang Terhadap Nilai Perusahaan pada Perusahaan Pertambangan Batubara yang Terdaftar di BEI Periode 2010-2013

The primary goal of the company is to maximize firm value. Firm value is investor perception of the company's success rate, it is often associated with stock prices. This research aim to examine the effect of leverage towards the value of the company in the coal mining companies which listed on the Indonesia Stock Exchange with periods 2010-2011. The hypothesis was leverage has negative effect on firm value. This research uses secondary data, of the coal mining companies which listed on BEI with periods 2010-2013. Research sample of 15 companies, where the method used is purposive sampling is a sampling method that takes an object with the specified criteria. Leverage are measured using Debt to Equity Ratio (DER) and Debt to Asset Ratio (DAR). Meanwhile, firm value is measured using Price Earning Ratio (PER). Analysis of the data used to analyze the factor that affect firm value is simple linier regression analysis and hypothesis test used the t-statistic for testing the partial regression coeffiecient at level of significant 5%. The results of this research finds leverage (DER) has negative significant effect on firm value (PER) with predictive ability is 6,8% and it shown by R square, and leverage (DAR) has negative significant effect on firm value (PER) with predictive ability is 7,2% and it shown by R square.

On False Alarm Rate of Matched Filter Under Distribution Mismatch

The generalized likelihood ratio test (GLRT) is a very widely used technique for detecting signals of interest amongst noise, when some of the parameters describing the signal (and possibly the noise) are unknown. The threshold of such a test is set from a desired probability of false alarm Pfa and hence this threshold depends on the statistical assumptions made about noise. In practice however, the noise statistics are seldom known and it becomes crucial to characterize Pfa under a mismatched distribution. In this letter, we address this problem in the case of a simple binary composite hypothesis testing problem (matched filter) when the threshold is designed under a Gaussian assumption while the noise actually follows an elliptically contoured distribution. We also consider the inverse situation. Generic expressions for the assumed and actual Pfa are derived and illustrated on the particular case of Student distributions for which simple, closed-form expressions are obtained. The latter show that the GLRT based on Gaussian assumption is not robust while that based on Student assumption is.

New multiple testing method under no dependency assumption, with application to multiple comparisons problem

Traditional multiple hypotheses testing mainly focuses on constructing stepwise procedures under some error rate control, such as familywise error rate (FWER), false discovery rate, and so forth. However, most of these procedures are obtained in independent case, and when there exists correlation across tests, the dependency may increase or decrease the chance of false rejections. In this paper, a totally different testing method is proposed, which doesn’t focus on specific error control, but pays attention to the overall performance of the collection of hypotheses and the structure utilization among hypotheses. Since the main purpose of multiple testing is to pick out the false ones from the whole hypotheses and present a rejection set, motivated by the principle of simple hypothesis testing, we give the final testing result based on the estimation of the set of all the true null hypotheses. Our method can be applied in any dependent case provided that a reasonable \(p\)-value can be obtained for each intersection hypothesis. We illustrate the new procedures with application to multiple comparisons problems. Theoretical results show the consistency of our method, and investigate their FWER behavior. Simulation results suggest that our procedures have a better overall performance than some existing procedures in dependent cases, especially in the total number of type I and type II errors.

Hypothesis testing for stochastic PDEs driven by additive noise

We study the simple hypothesis testing problem for the drift coefficient for stochastic fractional heat equation driven by additive noise. We introduce the notion of asymptotically the most powerful test, and find explicit forms of such tests in two asymptotic regimes: large time asymptotics, and increasing number of Fourier modes. The proposed statistics are derived based on Maximum Likelihood Ratio. Additionally, we obtain a series of important technical results of independent interest: we find the cumulant generating function of the log-likelihood ratio; obtain sharp large deviation type results for T→∞ and N→∞.