The Cusum Test for Parameter Change in Regression Models with ARCH Errors

This preliminary research investigates the application of Interactive Machine Learning (IML) to sonify the movements of visually impaired dancers. Using custom wearable devices with localized sound, our observations demonstrate how sonification enables the communication of time-based information about movements such as phrase length and periodicity, and nuanced information such as magnitudes and accelerations. The work raises a number challenges regarding the application of IML to this domain. In particular we identify a need for ensuring even rates of change in regression models when performing sonification and a need for consideration of how to convey machine learning approaches to end users.

The incidence of esophageal adenocarcinoma in the United States has risen rapidly over the last 30 years, whereas the incidence of esophageal squamous cell carcinoma has fallen dramatically. In contrast, parts of Asia have extremely high rates of squamous cell carcinoma, but virtually no adenocarcinoma. Within the United States, Asian-Americans as a whole, have low rates of esophageal adenocarcinoma and higher rates of squamous cell carcinoma. It is unclear what the patterns are for those Asians born in the United States. The relative influence of ethnicity and environment on the incidence of esophageal cancer in this population is unknown. We identified all cases of esophageal adenocarcinoma and squamous cell carcinoma from the California Cancer Registry 1988-2004, including 955 cases among 6 different Asian ethnicities. Time trends were examined using Joinpoint software to calculate the annual percentage changes in regression models. Rates of esophageal squamous cell carcinoma varied substantially among different Asian ethnic groups, but squamous cell carcinoma was much more common than adenocarcinoma in both foreign-born and US-born Asian-Americans. Rates of squamous cell carcinoma were slightly higher among US-born Asian men (4.0 per 100,000) compared with foreign-born Asian men (3.2 per 100,000) and White men (2.2 per 100,000), P = 0.03. Rates of adenocarcinoma were also slighter higher among US-born Asian men (1.2 per 100,000) compared with foreign-born Asian men (0.7 per 100,000), P = 0.01. Rates of squamous cell carcinoma decreased for both US-born and foreign-born Asians during this period, whereas adenocarcinoma remained low and stable. These results provide better insight into the genetic and environmental factors affecting the changing incidence of esophageal cancer histologies in the United States and Asia.

ABSTRACTDue to wide applicability and simplicity, the exponential distribution is the most commonly used distribution in reliability engineering and other life testing experiments. In this paper a test statistic for testing upper and lower outliers simultaneously in an exponential sample is proposed. However, the distribution of test statistic under the alternative is rather intricate, the null distribution is derived and critical values are obtained. A simulation study is also carried out to compare the performance of test and is found that the test based on this statistic is more powerful than the other two selected tests.

Approximating a class of goodness-of-fit test statistics

Finding a new drug which is both safe and efficient is an expensive and time-consuming endeavour. In particular, establishing the ‘dose-effect relationship’ – how beneficial a drug is at different dosages – can be challenging. Predicting this curve requires gathering experimental data by exposing and recording how cells respond to various levels of the drug. However, extreme values are often observed at low and high dosages, potentially introducing errors that are hard to correct in the prediction process. Yet, these extreme observations are sometimes genuine so researchers cannot just ignore them. To improve dose-effect estimation, Zhou, Liu, Fang et al. developed a new general-purpose approach. It uses advanced statistical modelling to account for extremes in lab data. This strategy outperformed other methods when dealing with these observations while also providing higher efficiency in data analysis with more uniform data in experiments. To facilitate implementation, Zhou, Liu, Fang et al. set up a user-friendly tool baptized ‘REAP’; this free online resource allows scientists without advanced statistical experience to harness the new approach and to perform dose-effect analysis more easily and accurately. This could boost research across many different disciplines that examine the effects of chemicals on cells.

In this paper, a new method of sensing primary user's signal for cognitive radios in Grassmann manifold is proposed. The Grassmann covariance matrix (GCM) is formed with the help of covariance matrix of the transmitted and the received symbols. By using GCM, a new test statistic is defined, which is the modification of Binet-Cauchy metric. On the basis of this new test statistic, primary user's signal is detected. We also show that the new test statistic is a valid detector since it follows the concentration phenomena. We derive the distribution of new test statistic under null hypothesis and alternative hypothesis. Lower bound for the probability of detection of signal is also derived using separating function and distribution of new test statistic. Simulations using the real-world measurements of digital television (DTV) signal show performance gain of the proposed method in terms of signal detection over existing methods and their agreement with the derived distribution. Additionally, we extend the proposed method for the cooperative spectrum sensing and derive the distribution under both hypotheses. Experimental verification on the software defined radio is also performed and it is found that the proposed method fulfills the requirement of maximum protection of the DTV signal.

The educational backwardness of Nigeria has been attributed to exchange rate depreciation. The objective of this study is to ascertain the nature and extent to which exchange rate is related to educational development in Nigeria from 1986 to 2022 using autoregressive distributed lag (ARDL) bounds test approach. The theoretical framework of the study is the dependency theory. Literacy rate is a proxy of educational development and the data of literacy rate was obtained from macrotrends.net. The data of exchange rate and the control variables were obtained from World Development Indicators. The domestic credit to private sector by banks, exchange rate, GDP per capita growth and lending interest rate had no relationship with educational development in Nigeria in both the short run and long run. Exchange rate does not matter to educational development in Nigeria. Exchange rate policy cannot be used to improve educational development in Nigeria. The dependency theory cannot explain educational development in Nigeria.

In order to analyze multivariate data for the randomized block design, a test based on permutation procedures was introduced by Mielke and Iyer (1982). Under the null hypothesis of no difference in the treatment effects, the distribution of this test statistic assigns equal probabilities to all possible permutations. When the number of blocks is large, this approach becomes unreasonable. In order to approximate its distribution, they obtained the first three moments of this test statistic under the null hypothesis. Due to negative skewness, the distribution of this test statistic was approximated by the Pearson type III distribution. We obtain the fourth moment of this test statistic for the case of three treatments and obtain a better approximating distribution. Then, we compare the power performance of this test based on this additional information with that based on three moment results for simulated samples from several underlying populations.

We develop two joint tests for the parametric drift and volatility functions of a diffusion model based on empirical processes. One key feature of our joint tests is that they account for different convergence rates of parameter estimators. The tests are of classical Kolmogorov–Smirnov and Cramér–von Mises types, and are asymptotically distribution free. The proposed tests have nontrivial power against a class of local alternatives with different convergence rates for the drift and volatility terms. Monte Carlo simulations show that the tests perform quite well in finite samples and outperform the nonparametric test of Hong and Li. The new tests are applied to EUR/USD exchange rate data and generate some interesting empirical findings that are consistent with our theoretical results and simulation studies.

ABSTRACTThis study considers the problem of testing for a parameter change in integer-valued time series models in which the conditional density of current observations is assumed to follow a Poisson distribution. As a test, we consider the CUSUM of the squares test based on the residuals from INGARCH models and find that the test converges weakly to the supremum of a Brownian bridge. A simulation study demonstrates its superiority to the residual and standardized residual-based CUSUM tests of Kang and Lee [Parameter change test for Poisson autoregressive models. Scand J Statist. 2014;41:1136–1152] and Lee and Lee [CUSUM tests for general nonlinear inter-valued GARCH models: comparison study. Ann Inst Stat Math. 2019;71:1033–1057.] as well as the CUSUM of squares test based on standardized residuals.

Most of the existing methodologies for evaluating heterogeneity in zero-inflated Poisson (ZIP) models are often assuming that the Poisson mean is a function of nuisance parameters. However, these nuisance parameters can be misspecified when performing these methodologies, the validity and the power of the test may be affected. In this article, we primarily focus on investigating the impact of misspecification on the performance of score test for homogeneity in ZIP models. Through an intensive simulation study, we find that: 1) under misspecification, the limiting distribution of the score test statistic under the null no longer follows a distribution. A parametric bootstrap methodology is suggested to use to find the true null limiting distribution of the score test statistic; 2) the power of the test decreases as the number of covariates in the Poisson mean increases. The test with a constant Poisson mean has the highest power, even compared to the test with a well-specified mean. At last, simulation results are applied to the Wuhan Inpatient Care Insurance data which contain excess zeros.

A new distribution free inferential procedure for comparing paired observations, called the ‘rank difference test’, is presented. The new procedure is based on ranks, and is applicable as a robust alternative to the related f test in all situations where the Wilcoxon signed ranks test is applicable. In additions it may be applied to ordinal data or operational measures which do not meet the assumptions underlying the Wilcoxon. Since the rank difference statistic, D, can take on half‐integer, as well as integer, values, it has continuity advantages over the Wilcoxon for small samples. The exact null distribution of the rank difference test statistic, D, is derived and tabulated for samples sizes from 2 to 7 pairs. The null distribution of the rank difference test statistic is shown to be asymptotically the same as that of the Wilcoxon test statistic for large sample sizes. Permutation methods are used to derive the null distribution for sample sizes from 8 to 20 based on 400000 simulations for each sample size. The practical application of the rank difference test is evaluated by comparing its power and performance with that of the Wilcoxon, the sign test and the related t test, for several classic sets of data in the literature, and for several sets of simulated data.

This paper studied the impact of inflation rates and US Dollar exchange rates in Indonesian stock market return volatility in the period of 2002-2012. Daily data of stock market return, inflation rates and US Dollar exchange rates were used to estimate the daily volatility of the stock return. The data of stock market price, inflation rates and US Dollar exchange rates were taken from Yahoo Finance, Indonesia statistics bureau and central bank of Indonesia, respectively. The result showed that both inflation rates and US Dollar exchange rates did not have a significant impact on the Indonesian stock market return volatility. Inflation rates contributed 0.03% change in daily stock market return volatility whereas a 1% appreciation of Rupiah contributed to a 0.0000001% change in daily stock market return volatility. This research finding was different from the result in Nigeria where Yaya’s & Shittu’s (2010) research had the same variables. Inflation rates and US Dollar exchange rates were to examine the impact on Nigeria stock market index return volatility.

The likelihood ratio test for hidden Markov models in two-sample problems

An exhaustive unconditional permutation distribution of a test statistic is necessary in the construction of exact tests of significance. Tables of exact critical values for the Mood test are scarce. In this paper, the exact permutation distribution of the Mood test statistic is generated empirically by actually obtaining all the distinct permutations of the variates in an experiment. The tables of exact critical values for the Mood test are produced.